Contemporary workplace resistance to new technology can be illustrated through several specific case studies.

For instance, Amazon discontinued its AI resume screening tool in 2018 after it became apparent that the system was disadvantaging female applicants.

The AI, trained on historical hiring data characterized by male-dominated success, amplified existing gender biases rather than removing them.

This incident highlighted the need for scrutiny and resistance to automation that lacks oversight and fairness.

Likewise, tools like HireVue, which use facial and voice analysis for candidate screening, have drawn regulatory complaints and public backlash over concerns regarding opaque algorithms and potential discrimination, prompting some workplaces and applicants to push for halting or revising their use .

Employee surveillance has also incited resistance.

One notable example involved an online education firm that, during the shift to remote work, mandated that employees' photos be taken every five minutes to monitor activity.

Employees who missed several check-ins were penalized, provoking widespread criticism over privacy, consent, and fairness.

This case generated significant attention on social media and highlighted the need for clear legal and ethical boundaries around digital monitoring.

Similar anxieties have led some countries, such as China, to begin enforcing laws and guidelines that require employers to justify monitoring practices, solicit employee consent, and introduce transparency and ethical oversight for workplace technology .

Organizations have at times addressed these concerns effectively.

During the COVID-19 pandemic, some companies implemented anonymous Bluetooth wristbands for contact tracing, reassuring staff that individual identities would only be revealed in the event of a positive case and only with a clear public health rationale.

This model—combining transparency, staff consent, and strict limitations on data exposure—proved successful in blending the legitimate needs of health and safety with employee privacy, mitigating resistance and fostering more positive workplace engagement .

In large organizations, resistance to AI systems frequently takes the form of skepticism from staff.

At L'Oréal, employees were hesitant both to participate in AI development and to accept AI models after errors were discovered, reflecting mistrust of both automation and corporate motives.

The company responded with a large-scale education initiative, establishing a “tech and data university” for all employees.

By prioritizing digital literacy and open dialogue about AI’s strengths and weaknesses, L'Oréal improved acceptance, demonstrating the importance of upskilling, communication, and transparency in managing resistance .

Ethical concerns surrounding technology in the workplace increasingly shape both employee resistance and regulatory responses.

Algorithmic bias is a well-substantiated problem; beyond the Amazon example, multiple recruiting algorithms have been found to discriminate on the basis of gender or race.

As a result, U.S. agencies such as the Equal Employment Opportunity Commission (EEOC) now require regular third-party audits for AI recruiting tools, and some city and state laws mandate transparency and publication of audit results.

These steps have emerged in response to worker and societal resistance, reflecting a demand for more accountable and fair technological deployment .

Another area of ethical tension is workplace surveillance.

Covert monitoring—such as keystroke logging, continuous webcam snapshots, or undisclosed tracking—can erode trust and diminish morale.

Best practices to address resistance in this area emphasize employee input, complete transparency about the scope and limits of monitoring, anonymization where possible, and reliance on employee consent except where legally necessary.

Employers who embed these values into practice and provide clear communication are more likely to see cooperation and acceptance than those who impose surveillance unilaterally or opaquely .

Generational differences are pronounced in the adoption and perception of workplace technology.

Surveys by Gartner and others reveal that younger employees, such as Millennials and Generation Z, generally adapt to new digital tools and remote work platforms more readily than older cohorts.

However, younger staff may lack the long-term institutional memory and in-person interaction skills that their older colleagues provide.

Older employees, sometimes slower to transition, offer stability and contextual wisdom in periods of rapid digital transformation.

Research also highlights that high levels of job-related stress are found across all age groups, sometimes heightened for older workers asked to quickly adopt unfamiliar digital systems.

To respond, organizations such as those in Singapore have pursued proactive and inclusive reskilling programs for all ages, using government-industry partnerships and internal learning platforms to smooth transitions and prevent generational exclusion .

From an organizational perspective, several best-practice approaches have proven valuable in addressing resistance.

Leaders can convene open forums or listening sessions to gather employee concerns before, during, and after technology rollouts.

Establishing proactive education and upskilling programs—tailored for both technical and non-technical workers—empowers employees to master new tools rather than feel imposed upon.

Notably, phased pilots and invitation for employee participation in testing new systems help to build confidence and trust.

Transparency about ethical, data privacy, and surveillance issues—sometimes formalized via an “employee data bill of rights”—further ensures that adaptations respect individual autonomy and legal norms .

Labor unions play a central role in shaping the conversation about technological change.

Unions increasingly negotiate for collective bargaining power over the introduction and oversight of automation and AI.

They may insist on retraining, compensation, and human-in-the-loop safeguards, and often serve as advocates for worker input during digital transformation.

Internationally and in the U.S., union pressure has driven more balanced floor-level technology policies and protections against bias, unemployment, and loss of workplace dignity resulting from unregulated tech adoption .

In conclusion, the experience of 21st-century workplace Luddites is best understood as multifaceted and deeply contextual.

Their resistance, typically grounded in legitimate concerns over job security, fairness, autonomy, privacy, ethics, and generational transitions, mirrors both past and present anxieties about the disruptive potential of technology.

Rather than being reflexively anti-progress, many are advocates for more inclusive, accountable, and responsible technological change.

Organizations that acknowledge and address these concerns through transparency, education, phased implementation, and genuine employee input—often with union involvement—tend to foster healthier workplaces and more successful digital transformation.

This approach not only reduces conflict but can build a more trusting, adaptable, and innovative organizational culture, which ultimately benefits both employees and the business.

Contemporary workplace resistance to new technology can be illustrated through several specific case studies.

Title:

Content: Brian Merchant, author: “Luddites are more dangerous to the rich than Robin Hood”

The Luddites have been a great historical resource. Any showy enemy of anything that looked like technological progress has been called a Luddite. Today with generative artificial intelligence and its critics, the word has been revived. The author and journalist Brian Merchant (Iowa City, USA, 1983) saw it coming and has written a book that reviews the Luddite revolt in England at the beginning of the 19th century and its similarities with current technologies: Blood in the Machine (Blood in the Machine, currently without a planned edition in Spanish) thus explores the origins of the current revolt against big technology.

Although the book has appeared in the midst of the AI hype, Merchant began his research a decade ago: “Uber was growing and it was beginning to be seen that it was having a hard impact on taxi drivers, I heard the term Luddite from the pro-Uber sector and wrote an article titled You don't understand anything about Luddites”. Now in his book he remembers that the Luddite revolt was a labor struggle, not anti-technology, at a time when unions were prohibited. In a conversation with EL PAÍS from his residence in Los Angeles, Merchant clarifies the historical impact of the Luddites and the possible similar struggles that are being played today in Silicon Valley.

1. The Luddites were not against progress

The labor revolt over the loss of rights was more important than the destruction of textile machines: “The biggest mistake about the Luddites is to say that they were against technology and progress,” says Merchant. “They were against certain technologies because they recognized the way in which they were going to be used in their context against them as workers. It was a labor movement, against poverty, not anti-technology. The British Government began to mold them in this way almost immediately, it was keen to mock the Luddites and present the argument that they were against progress. It is very similar to how today people in positions of power in technology companies accuse those who complain about poor working conditions. It's the same story for 200 years.”

The book quotes a phrase from historian Eric Hobsbawm that explains this stage: “It was collective bargaining through riots.” According to Merchant, this tactic in a workers' struggle was very powerful for two reasons: “First, it was symbolic because everyone understood that what they were destroying was not just a machine. It was also the specific machine used to transfer wealth from the workers to the employer. Second, it was useful because they destroyed the equipment that businessmen needed.”

2. Why Robin Hood is better accepted than Ned Ludd

Ned Ludd was a legendary character. No one knows for sure if he existed. It was the legend of a teenager who would have broken the first machines at the beginning of the Industrial Revolution. In 1811, rebellious workers used his legendary name to sign letters and further scare: “Ludd probably never existed. But the incarnation of it would become a mascot, an organizational tool, a wink: a powerful 19th-century meme,” Merchant writes.

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Ludd lived near Nottingham (United Kingdom), like another legendary figure who has been better treated by history. There must be a reason, says Merchant: “The Luddites organized a popular and powerful rebellion. People wrote popular songs about them. They were like Robin Hood. But it is interesting that Robin Hood has endured as a positive light, while the Luddites have not. He suggests that the myth of Ned Ludd is in some ways more dangerous to the elites and the wealthy than that of Robin Hood, which is a little more nebulous and open. The Luddites say: go ahead and break the machines of your oppression. Robin Hood says: steal from the rich and give to the poor. “I think that’s a little more tolerable.”

3. How are they similar to today's problems?

The background of the book is that the history of the Luddites is similar to what happens today in Silicon Valley, where a small group of corporations dominate new technologies. “The way technologies are developed has basically not changed. You have a handful of people with access to enough capital or political influence to be able to build a factory and use automation technologies. “Back then, the most aggressive owners didn’t ask anyone how to find a way to make this a win-win.”

Merchant has received criticism for equating the great businessmen of 19th century England with 21st century America. But there are details that he does not see that far away: “The technology development process is in some ways even more undemocratic today than then. There is this immense pool of capital available to Silicon Valley innovators through venture capital. Uber wasn't profitable for 10 years, but they had access to that capital. Did anyone really like that? Did it work for workers and delivery people? Nothing mattered, they just had more money and moved on.”

See also  Just the mystery

For instance, Amazon discontinued its AI resume screening tool in 2018 after it became apparent that the system was disadvantaging female applicants.

Title:

Content: December 15, 2021

Volume XI, Number 349

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December 14, 2021

December 13, 2021

Brave New World: The EEOC's Artificial Intelligence Initiative

The use of artificial intelligence (“AI”) and machine learning in the workplace is growing exponentially – and specifically in hiring. Over the last two decades, web-based applications and questionnaires have made paper applications nearly obsolete. As employers seek to streamline recruitment and control costs, they have jumped to use computer-based screening tools such as “chatbots” to communicate with job applicants, to schedule interviews, ask screening questions, and even conduct video conference interviews and presentations in the selection process. Employers of all sizes are creating their own systems, or hiring vendors who will design and implement keyword searches, predictive algorithms and even facial recognition algorithms to find the best-suited candidates. The algorithms in these computer models make inferences from data about people, including their identities, their demographic attributes, their preferences, and their likely future behaviors. Now faced with the impact of COVID-19 and the growing talent shortage, employers may see the use of AI as a more efficient way through the hiring process.

However, the use of AI technology is controversial. Researchers have voiced increasing concerns about the inherent risk of automating systemic bias. Because algorithms rely on historical datasets and human inputs, the technology can generate bias, or exacerbate existing bias. AI systems can generate unfounded conclusions that may be based on protected characteristics.

In 2016, recognizing these concerns over bias, the U.S. Equal Employment Opportunity Commission (the “Commission” or “EEOC”) started to examine the impact of AI, people analytics and “big data” on employment. “Big data” was characterized as the use of algorithms, data scraping of the internet, and other technology-based methods of evaluating huge amounts of information about individuals. The Commission acknowledged in its 2016 systemic program review, that “[h]iring or nonselection remains one of the most difficult issues for workers to challenge in a private action, as an applicant is unlikely to know about the effect of hiring tests or assessments, or have the resources to challenge them.” Despite this and some reports that the agency reportedly investigated the use of an algorithm in hiring in two cases in 2019, it took little further action until 2021.

Meanwhile, some high-profile examples of AI bias flagged the risk. In 2018, Amazon stopped using an algorithmic-based resume review program when its results showed that the program resulted in bias against female applicants. In 2019, the Electronic Privacy Information Center filed an FTC complaint against recruiting company HireVue arguing that its face-scanning technology constituted an unfair and deceptive trade practice. HireVue’s “AI-driven assessments,” use video interviews to analyze hundreds of thousands of data points related to a person’s speaking voice, word selection and facial movements. With a lack of federal focus on the issue, in 2019 states began to generate legislation or resolutions. During 2021 at least seventeen states have introduced bills or resolutions, or enacted or implemented laws related to the use of AI.

Finally, on December 8, 2020, ten U.S. Senators sent a letter to then-EEOC Chair Janet Dhillon, calling on the agency to exercise its oversight authority regarding hiring technologies. The timing of the letter reflected the Senators’ concerns that businesses reopening after pandemic closures might seek to hire quickly with the potential upswing in use by employers who “turn to technology to manage and screen large numbers of applicants to support a physically distant hiring process.”

The Senators placed responsibility for addressing the risk of bias and discrimination from the use of hiring technologies squarely with the Commission, stating, “The Commission is responsible for ensuring that hiring technologies do not act as ‘built-in headwinds’ for minority groups. Effective oversight of hiring technologies requires proactively investigating and auditing their effects on protected classes, enforcing against discriminatory hiring assessments or processes, and providing guidance for employers on designing and auditing equitable hiring processes.”

Among other questions, the Senators asked the Commission to provide information about its use of “its authority to investigate and/or enforce against discrimination related to the use of hiring technologies.” The letter asked the Commission to identify what authority it could or has used to “study and investigate the development and design, use, and impacts of hiring technologies absent an individual charge of discrimination?” Notably, the letter also queried the Commission’s intention to issue guidance, regulations, and to conduct research.

Re-enter the EEOC. On October 28, 2021, EEOC Chair Charlotte A. Burrows announced a new agency initiative focused on ensuring that the use of AI by employers at all employment stages complies with federal anti-discrimination law. A companion press release outlined the EEOC’s five initial goals for its initiative, which were notably reflective of the type of action queried by the Senators:

  • Establish an internal working group to coordinate the agency’s work on the initiative;

  • Launch a series of listening sessions with key stakeholders regarding use of algorithmic tools and their employment ramifications;

  • Gather information about the adoption, design, and impact of hiring and other employment-related technologies;

  • Identify promising practices; and,

  • Issue technical assistance to provide guidance on algorithmic fairness and the use of AI in employment decisions.

In making the announcement Burrows stated, “The bottom line here, really, is despite this aura of neutrality and objectivity around artificial intelligence and predictive tools that incorporate algorithms, they can end up reproducing human biases if we’re not careful and aware that we need to check for that.” She characterized the risk-benefit analysis that must attach to use of AI saying, “Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment. At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs. We must work to ensure that these new technologies do not become a high-tech pathway to discrimination.”

Burrows’ comments follow 2021 comments and articles issued by fellow EEOC Commissioner Keith Sonderling, beginning in early Spring 2021. In September, Sonderling signaled that EEOC may use Commissioner charges — agency-initiated investigations unconnected to a discrimination charge — to ensure employers are not using AI unlawfully. Sonderling explained that using Commissioner charges would assist enforcement because job applicants and employees are often unaware that they have been excluded from a certain job because of flawed or improperly designed AI software or bias. In 2021 and prior to the agency’s announcement of the AI initiative, Commission investigators participated in AI training.

The public comments by the Commissioners clearly signal that the agency has brought its focus to the use of hiring and employment technologies as an area of systemic discrimination. When the agency does this, employers using AI or other technologies should exercise caution. Despite the initiative announcement characterizing the Commission as initially concentrating on information collection, education and guidance, the investigator training points to a companion focus of enforcement. Burrows signaled this, saying, “Bias in employment arising from the use of algorithms and AI falls squarely within the Commission’s priority to address systemic discrimination.” Employers subject to a Charge investigation should be alert to Commission inquiries into their use of technology.

Proactive Steps for Employers

In light of the Commission’s statements of priority, employers using hiring and employment technology systems, such as artificial intelligence or algorithmic decision-making systems, should evaluate the systems and their use to ensure there is no ensuing bias. Regular auditing should occur to ensure bias is not introduced at a later stage. Additionally, if an employer has used or is considering use of an AI technology vendor, the employer should: (1) ensure the vendor understands the employer’s EEO obligations; (2) ask the vendor to explain how it proactively avoids bias in its process and the results; and, (3) consider making the avoidance of bias a material term of the vendor contract.

Moreover, employers using hiring and employment technology systems should implement policies related to such use including requiring any managers using such technology to report any biased results and to prohibit inappropriate or discriminatory use of such systems.

Finally, given the relative volume of Commission activity in this area, it is anticipated that the agency may be issuing Guidance on use of hiring and employment technologies soon. Employers are advised to keep informed.

©2021 von Briesen & Roper, s.cNational Law Review, Volume XI, Number 348
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About this Author

Jill Pedigo Hall partners with businesses to develop and implement best practices, strategies, and solutions that ensure compliance with labor and employment laws while supporting business goals. She is recognized for leveraging her business acumen, insights, and experience to solve employment law issues and manage risks with an efficient, practical approach for employers of every size. She is widely regarded by her colleagues and clients as a perceptive and resourceful litigator and has an AV Pre-eminent® rating with Martindale-Hubbell, the highest peer rating possible...

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The AI, trained on historical hiring data characterized by male-dominated success, amplified existing gender biases rather than removing them.

Title:

Content: In recent years, Taiwan has kept up with the global digital wave, and has continued to develop intelligent technology. Even government departments have gradually introduced AI to play an auxiliary role. In the face of many controversies and criticisms, six teachers from the School of Social Sciences of Chung Cheng University took the initiative to discuss the ethics of digital technology.
Abroad has been able to produce 200 rapid reports in 1 second, there are AI judges sentenced criminals released from prison, more AI to review the eligibility of medical services, but in recent years, there is a lot of discussion on the harm of artificial intelligence to social justice. The project aims to explore the ethical issues of digital technology as a reference for the government to promote AI, so it has been funded by the Ministry of Science and Technology.
Li Cuiping, professor of political science at Chung Cheng University, for example, as early as 10 years ago, the US police had used big data to analyze criminal databases to detect potential criminal behavior in advance, but AI is not omnipotent, and all the data hide traditional deviations.
Li Cuiping said that traditionally, African-Americans entered the American continent as slaves, which has long caused people of color to be relatively poor, marginalized and discriminated against, and there are more African-Americans than whites in the case, so the original data of the database has long existed racial inequality, in addition, Amazon also used AI to screen job profiles, because of the traditional male workplace achievements are higher, and let AI inadvertently produce gender discrimination.
The team will not only focus on the injustice and injustice caused by artificial intelligence "non-intentional discrimination", but also from digital technology into daily life, replacing labor, etc., analyzing the problem of job change among the elderly, the relationship between journalists and digital tools, and the sense of fairness and job satisfaction of remote workers. Journalism can be a steal, making journalists a vulnerable category.
"While enjoying digital technology, we must not forget that there are a group of people whose human rights and right to work have become weak under digital technology," Li said. The plan is to highlight that under this global market mechanism, which emphasizes competition, freedom and capital flow, there is a new wave of inequality in the rise of digital technology.
# AI Artificial Intelligence # Ministry of Science and Technology # Zhongzheng University # Justice Crisis # Robotics

This incident highlighted the need for scrutiny and resistance to automation that lacks oversight and fairness.

Title:

Content: How Can We Turn The Tables On AI And Bias? Man-Made Author Tracey Spicer Explains How

Robyn Foyster Robyn Foyster has been verified by Muck Rack's editorial team
on 9 January 2024

Here is the fourth in a series of extracts from award-winning journalist Tracey Spicer’s book, Man-Made: How the bias of the past is being built into the future. In this extract from her book, Tracey explains how we can turn the tables on AI and bias.

In an era in which employers use workplace surveillance to monitor our productivity, we often forget that we can turn the tables. Futurists predict workers will soon be monitoring their bosses using the same tools. For example, employees can check and record stress levels on their Garmin, Fitbit or iWatch.

Perhaps there’s a place for a website to publish the results of smartwatch data from employees at particular companies. Kinda like a high-tech Glassdoor, the website where employees anonymously review companies. It’d be fascinating to see the results from the likes of Apple, Google and Facebook. Talk about turning the tables on the tech titans. Solidarity forever!

Nowadays, male allies are refusing to speak at conventions where there’s an unequal representation of genders. Advocates are highlighting this on social media using the hashtag #Whereareallthewomen. If you work in tech and are slated to present at a conference, ask yourself is there someone I can nominate instead? It might be a woman, a person of colour, or a colleague with a disability.

Ethical investors should be putting a gender lens over tech stocks. ‘What’s the composition of the executive team and board?’ ‘How many women are involved in research and development?’ ‘Does this company invest in organisations with a history of gender or racial bias?’ Talk to your bank, superannuation fund or financial advisor about your concerns. By the end of this decade, women will control US$30 trillion in financial assets. Yep – trillion.

Do you work in the university sector? Lecturers should be aware of the pitfalls of using machine learning to evaluate assessments. There needs to be human oversight. Sophisticated algorithms can pinpoint struggling students and organise assistance. However, they can also exacerbate existing bias, further side-lining these students.

It might be time for your uni to create a department dedicated to AI bias. The Human Technology Institute at the University of Technology Sydney is hitting the ground running with a world-leading report recommending reform on facial recognition, based on international human rights law. The report urges the federal attorney-general to lead this ‘pressing’ process.

Maybe you’re a lawyer working in the technology field, or a judge who’ll be presiding over cases of alleged algorithmic bias. It’s your job to decide what ‘fair’ means as it pertains to the use of artificial intelligence and machine learning. So too, insurers must adopt robust fairness criteria. One test being developed centres on something called ‘counterfactual fairness’. Yikes! What the heck is that? Apparently, it asks AI systems this question: if attributes such as race, gender or sexual orientation are changed, do the model’s decisions remain the same?

Where can you look for more details? The Alan Turing Institute’s website is an excellent source for information on fairness, transparency and privacy. Google AI and Fairness 360 provide open-source toolkits to check for bias in datasets and machine learning models. IBM’s Watson OpenScale performs bias-checking and mitigation in real time. Read the 2021 book co-authored by Dr Catriona Wallace, Checkmate Humanity: The how and why of Responsible AI.

One easy suggestion is to include a human in the loop: an actual person double-checking everything. The World Economic Forum wants businesses to be aware of the three main forms of machine bias. (Yes, there are more than three, but these are often evident in the workplace.) These are:

1. Implicit bias: A system that discriminates against a person or group on the grounds of gender, race, disability, sexuality or social class.

2. Sampling bias: This occurs when the data used to train the machine isn’t representative of the population the machine will be servicing. For example, we know about the problem with data from white middle-class hospitals failing to represent non-white populations from poorer neighbourhoods.

3. Temporal bias: Even if the AI produces fair results today, it may not do so in the future, as circumstances change.

Put simply, we should all be aware of the ‘red flags’, as my teenage daughter would say. Although she says it at the slightest provocation. I’ll try to give her a kiss on the cheek before bedtime: ‘Red flag, Mum!’ Gotta love Generation Alpha. These kids really know their boundaries!

If you think speaking out about red flags will make little difference, think again. Research by the Brookings Institution reveals public opinion is hugely influential in relation to legislation on AI fairness, privacy and safety in the US. Here in Australia, we’re at the beginning of the conversation. We can exert enormous influence in the coming years.

Follow the international change-makers and donate to their foundations. Hop onto ajl.org to give money to the Algorithmic Justice League, which is fighting for equitable and accountable artificial intelligence. Maybe you work in voice software. Anyone who identifies as ‘a female, non-binary, genderqueer, genderfluid, gender non conforming, agender, and all minority genders’ can be part of the community at womeninvoice.org.

You can buy Man-Made: How the bias of the past is being built into the futurehere

How to get a signed copy of Tracey Spicer’s book

Readers can also buy a signed copy via this link: https://traceyspicer.com.au/signedbook

You can read more extracts from Tracey Spicer’s book here on ‘How we can take action on AI being predisposed to ‘Misogyny, ableism, homophobia and ageism.’

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Likewise, tools like HireVue, which use facial and voice analysis for candidate screening, have drawn regulatory complaints and public backlash over concerns regarding opaque algorithms and potential discrimination, prompting some workplaces and applicants to push for halting or revising their use .

Employee surveillance has also incited resistance.

One notable example involved an online education firm that, during the shift to remote work, mandated that employees' photos be taken every five minutes to monitor activity.

Title:

Content: Leave some space for your home office.
Font Size:
Source: Guangzhou Daily
Author: Liu Ran Ran
Recently, a user on the workplace social platform broke the news that an online vocational education company will use the camera to capture the face every 5 minutes during the staff's home office.
If you can ’ t catch a few times, you have to deduct the full performance; not enough 89 counts as absenteeism.
The home office has repeatedly been exposed to strange regulations, it can no longer be regarded as a company's "family affairs", but involves the protection of personal information and the protection of workers' rights and interests.
The Civil Code and the Personal Information Protection Law, which came into effect last November, require that the collection and use of personal information, in addition to being lawful, legitimate and necessary, must also express and obtain consent, and adopt methods that have the least impact on the rights and interests of individuals.
Take this case, the employee may not have objected on the spot, but can not help but spit on the workplace social platform, in fact, it is a clear attitude.
Businesses cannot ignore this.
From the point of view of the enterprise, employees dare not say, of course, it is difficult to think about a place and drive to a place.
A company, if the digital means are used to monitor employees to prevent sabotage, and the priority of attendance is placed on the actual results, it is actually putting the cart before the horse, and can not let employees have a sense of belonging, which is more harmful to the long-term development of the enterprise.
Scientific and technological means are changing with each passing day, and enterprise management should also be actively innovated.
This year, the General Office of the Communist Party of China (CPC) Central Committee and the General Office of the State Council issued the Opinions on Strengthening the Governance of Science and Technology Ethics.
The development of science and technology for good, in addition to the need for social awareness, but also the need for regulatory departments to further liberate people's productive forces with ethics first and agile governance, and effectively protect the legitimate rights and interests of the masses.
Relevant departments should innovate the supervision mode, improve the efficiency of supervision, and guide technical means not to go astray, so as to better serve social life.
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Employees who missed several check-ins were penalized, provoking widespread criticism over privacy, consent, and fairness.

This case generated significant attention on social media and highlighted the need for clear legal and ethical boundaries around digital monitoring.

Title:

Content: As workplace volatility continues in 2023, Gartner, Inc. has revealed its top nine workplace predictions that HR leaders must address this year and beyond to successfully navigate today’s labour market and continue to drive business outcomes.

“HR leaders have faced an increasingly unpredictable environment amid many organizations mandating a return to office, permanently higher turnover and burnt out employees,” says Emily Rose McRae, senior director in the Gartner HR practice. “This year’s predictions highlight the aspects of work that HR leaders must prioritise over the next 12 months.”

The top nine predictions for HR leaders are:

‘Quiet hiring’ will create new avenues to snag in-demand talent

Despite worries about a forthcoming recession and some layoff announcements, Gartner benchmarking data shows that a majority of HR leaders still expect the labour market to get more competitive.

Progressive HR leaders will turn to “quiet hiring” to acquire new skills and capabilities without acquiring new full-time employees. For example, they will deploy current employees to the highest priorities, which may necessitate reskilling and stretch assignments.

Leaders will also emphasise upskilling to fulfill employees’ career aspirations while meeting organisational needs.

Hybrid flexibility will reach frontline workers

Many organizations have sought to make the workforce-wide experience fair by simply making it equal: mandating on-site work for those who could work elsewhere. More than six in 10 organisations have some sort of on-site requirement for employees whose work can be done remotely.

In 2023, smart organisations will stop limiting flexibility in the name of fairness and will pursue formal strategies for more flexibility for the frontline workforce. To do this, organisations will provide frontline workers more control over their schedules, more paid leave and more stability in work schedules.

Managers will be sandwiched by leader and employee expectations

“Many managers are struggling with how to balance the need to implement corporate strategy on behalf of senior leaders and providing the sense of purpose, flexibility, and career opportunities that their employees expect,” says Peter Aykens, chief of research in the Gartner HR practice.

In 2023, leading organizations will recognize the increasing pressure on managers, and they will provide support and training to mitigate the widening managerial skills gap while clarifying manager priorities and redesigning their roles where necessary.

Pursuit of non-traditional candidates will expand talent pipelines

Organisations are being forced to expand and diversify their talent pipelines due to employees increasingly charting non-linear career paths. These organisations are also faced with an inability to meet talent needs through traditional sourcing methods and candidate pools.

To fill critical roles in 2023, organisations will need to become more comfortable assessing candidates solely on their ability to perform in the role, rather than their credentials and prior experience.

Organisations will take several approaches to do this, such as relaxing formal education and experience requirements in job postings and reaching out directly to internal or external candidates from nontraditional backgrounds.

Healing pandemic trauma will open a path to more sustainable performance

The challenges of a global pandemic, including unemployment, supply chain shortages, isolation from family and friends and divisive political fractures created intense stress. In fact, in 2022, employees’ stress and worry grew above even 2020 peaks — nearly 60% of employees are stressed at their jobs every day.

This year, leading organisations will shift from offering rest as a recovery solution and instead will support proactive rest for employees to help them maintain their emotional resilience and performance.

A July 2022 Gartner survey of nearly 3 500 employees found that when organisations offer proactive rest, they see a 26% increase in employee performance.

Organisations will drive DEI forward amid growing pushback

Although organisations still prioritise diversity, equity and inclusion (DEI) efforts, many employees say their organisation’s DEI efforts are divisive. This pushback to DEI efforts can decrease workforce engagement, inclusion and trust.

To address this fraught moment for DEI, in 2023, HR must equip managers with tools and strategies to engage resistant employees and address pushback early before it evolves into more disruptive forms of DEI resistance. This is crucial for maintaining the momentum of DEI efforts and achieving greater maturity and strategic impact.

As organisations get more personal with employee support, it will create new data risks

Organisations are increasingly using emerging technologies – artificial intelligence (AI) assistants, wearables, etc. – to collect more data on employees’ health, family situations, living conditions and mental health in order to respond more effectively to their needs.

However, using these technologies has the potential to create a looming privacy crisis.

Progressive organizations will use 2023 to create an employee data bill of rights that prioritises transparency around how they collect, use and store employee data, and which allows employees to opt out of practices they find objectionable.

Concerns around AI will lead to increased transparency in recruiting tech

With more organisations leveraging AI in recruiting, the ethical implications of these practices have become more urgent. For example, a new law in New York City went into effect on 1 January that limits employers’ use of AI recruiting tools and requires organizations to undergo annual bias audits and publicly disclose their hiring metrics.

Organisations that use AI and machine learning in their hiring processes, as well as the vendors they rely on for these services, will face pressure to get ahead of new regulations and be more transparent about how they are using AI – and give employees and candidates the choice to opt out from AI-led processes.

Gen Z skills gaps will reveal workforce-wide erosion of social skills

The rise in remote and hybrid work has meant that many new-to-the-workforce employees have had few in-person opportunities to observe norms and determine what is appropriate or effective within their organisations.

Rather than forcing employees back to in-person work to establish connections, leaders need to build intentional connections among employees across geographic – and generational – boundaries.

Gartner research shows that there are three key elements to creating intentional interactions among employees: employee choice and autonomy, a clear structure and purpose, and a sense of levity and fun.

Similar anxieties have led some countries, such as China, to begin enforcing laws and guidelines that require employers to justify monitoring practices, solicit employee consent, and introduce transparency and ethical oversight for workplace technology .

Title:

Content: Technology in the face of democracy
In September 2021, data scientist Frances Hogan resigned her position at Facebook and then revealed tens of thousands of pages of the company ’ s documents, research and correspondence, which were submitted to Congress and the Securities and Exchange Commission.
The documents, which sparked a storm of hate and lawsuits against Facebook and its CEO, indicate that the platform not only affects the mental health of adolescents, enables the spread of populist hate speech, and enhances the work of human trafficking gangs, but the company is aware of these drawbacks, and is either satisfied with them or unable to reduce them; contrary to what Mark Zuckerberg says about the platform ’ s ability and algorithms to provide a safer environment for teenagers. To contribute to the promotion of democratic values and the reduction of hate speech.
In this article, I do not want to try to prove the company ’ s intention or claim, but rather to trace the reasons that made such phenomena possible, whether on Facebook, Instagram or other social media platforms; which will lead us to the conclusion that this case does not apply to Facebook alone, and it is not related to someone ’ s intention or disability, but it is a fundamental characteristic of all the tech giants and determine how they work and dominate their business model. This is what the American economist Shoshana Zuboff calls “behavioral surplus value. ”
In Zuboff ’ s view, the tech giants of today ’ s world are operating on a single business model that Google established after the dot-com bubble in 2008 — one that doesn ’ t stop selling consumer data, information, and surveillance — what Zuboff calls “surveillance capitalism, ” but rather establishes what Zuboff calls the“ prediction goods market, ” a market that relies on collecting user information from social networks and converting it into algorithms. able to predict their behavior.
These algorithms and their results are then sold to marketers, contestants in election races, or authoritarian governments to monitor consumers more broadly and make their control more monopolistic in the market.
Zuboff ’ s analysis will be very useful in understanding the phenomenon, which leads us to trace its historical roots in the first section of the article.
It seems that we will be facing a fundamental thesis that we will have to criticize, and this criticism is the first objective of the article.
There is a thesis led by Zuboff herself and shared by many of the top technicians in these companies, and a number of commentators and democratic politicians, all of whom argue that the phenomena revealed by the Hogan documents and other similar phenomena are real, but they seem to see it as a tax to be paid for the promised fruits of the technical world, and that algorithms are able to correct its course by themselves, especially Zuboff. Inside it is led by the “top minds of the business world. ”
The second argument the article will address is that a mere commodity such as the “prediction market commodity ” is hostile to democracy, the rule of law, equality, and privacy.
It is true that “surveillance capitalism ” is part of monopoly capitalism, but I would argue that it goes beyond this, and that its effects extend from the economy to all spheres of social life, especially its negative effects on the future of democracy.
The third thing we focus on is that falling into a pessimistic vision of humanity ’ s future under technology is just another facet of the rosy narrative promoted by tech giants of the story of the inevitable rise of technology, but in its pessimistic version.
The emergence of the forecasting market and behavioral surplus value
After the rise of neoliberal capitalism following the waves of inflation and recession in the 1970s, the authors of neoliberal programs decided that the policies of state intervention in the economy and the extension of social protection for the people were no longer profitable.
Neoliberalism retained the core elements of classical economic liberalism, such as freedom and individualism, but the role of the state in social protection and welfare programs was hotly contested by policymakers and legislators.
While some countries in Europe have pursued so-called systemic liberalism aimed at maintaining and expanding social protection programmes, notably Germany, Britain and the United States have gone the other way, reducing the role of the state in the economy and expanding market freedom, trade and international lending institutions at the expense of social spending.
Hence the story of monopolistic technology as we know it today began, coinciding with the collapse of the value of the shares of major technology companies in the so-called dot-com bubble in 2001, which was followed by severe frustration with the future of technology and its promise and ability to develop capitalism.
After that, Google took a step that will henceforth define the future of tech companies and their practices; the move is to abandon its hostility to advertising gains due to shareholder pressure.
Google has decided to increase advertising revenue through hell, which has opened the door for the rest of the companies to follow suit by monopolizing access to its users ’ data and using it in online promotion and marketing services, which has long been known as “data exhaustion. ”
Using its superior analytical capabilities, Google has adopted a new way of making money by building algorithms that can predict consumer behavior; from monitoring your online activity, for example, the algorithms recognize your buying habits and preferences and ways of diversifying your sources of consumption, and of course, the level and types of spending, and even your reluctance to buy a particular product when you receive an ad for another.
These algorithms provide sellers with not only real-time information about consumer preferences, but also predictions of consumer preferences in the near future.
With this behavioral revolution, excess data has become the basis for new prediction markets that we now know as targeted advertising.
This is what Zuboff calls “behavioral surplus-value, ” where a user ’ s online behavior, obscene and monopolized data, becomes a mine for value extraction and profit.
Google does not care about the consumer and his decisions, and this data is often taken without consent or acknowledgement from the consumer.
This new type of “prediction goods market ” quickly became a role model for all major Silicon Valley companies, and advertising became as important to Google as the development of the search engine on which the company was founded.
When Google went public in 2004, the company revealed that after adopting the plan, it increased its revenue from 2001 to 2004 by more than 3,500 percent.
Zuboff points out that Google CEO Sheryl Sandberg ’ s move to Facebook represented a magical, contagious transition to this new plan based on behavioral surplus value extraction, or “prediction goods trade, ” which is no longer limited to Silicon Valley companies since 2008, but has moved to sectors such as insurance, retail, healthcare, finance, entertainment, education, transportation, and so on, creating a“ new production pattern. ” Zubov is a whole “new environment ” of suppliers, producers, customers and market makers.
In that market, every product or service starts with “smart ” or“ customized, ” every device connects to the internet, and every “digital assistant ” is simply an interface for a supply chain of behavioral data that helps predict the future of what Zuboff calls the“ surveillance economy. ”
The “prediction goods market ” merges three categories into one:“ user, consumer, and consumer data, ” and this triad is henceforth just multiple facets of a single phenomenon, which Zuboff calls “indigenous people ” in the style of the colonial invasion and exploitation model.
Today we do not lack a plethora of expressions that attempt to describe what the technological market has changed the structure of global capitalism.
We will find “algorithmic capitalism, ”“ cognitive capitalism, ” “communicative capitalism, ”“ data capitalism, ” “digital capitalism, ”“ information capitalism, ” “platform capitalism, ”“ surveillance capitalism, ” and “virtual capitalism, ” to name a few.
Recently, then, capitalism is abandoning itself and retreating into a world of “data emperors, ” which is why this phenomenon has been called“ digital feudalism, ” “technological feudalism, ”“ information feudalism, ” or simply “new feudalism. ”
But if tech giants like Google, Facebook, and Amazon are not accumulating their wealth through capitalism ’ s old exploitative mechanisms of labor itself, and while today ’ s production process is not based on manufacturing, but on monopolistic control over data sets and algorithms to extract advertising revenues generated elsewhere in the capitalist economy, is this a new stage of capitalism?
The big companies that rely on technology do not make a profit, but their market value continues to rise due to speculation in the markets, representing what is known as the model of "selling growth", is this model considered a transgression of capitalism?
Is it a new form of “digital expropriation ” that fuels a new logic of accumulation that can be seen as a structural change in the structure of capitalism, similar to the model of“ accumulation by plunder ” posited by David Harvey in The New Imperialism?
Or is it, as Cedric Durand put it in his book “Technical Feudalism, ” that the tech giants are effectively subjecting us to their rule by controlling information and knowledge in a“ knowledge monopoly, ” thus creating ever more sophisticated means of seizing surplus value without getting involved in the production process?
Darwinism and counter-Darwinism
The main problem of technology today, and the way it extracts value through monopoly, is not merely that it is a monopoly, not that it leads us to a social reality similar to Big Brother ’ s world, but that it is based on a structural transformation of the labor market.
The merging of knowledge with technology and markets means that everyone who has nothing to sell will be despised, and a new kind of individualism will triumph, not the “liberal individualism ” promised by the liberal project, but a technical individualism that cares so much about the“ user-consumer-data ” triad. Achille Mbembe: “The Nasi ” who will be every day the firewood of a new“ trend ”, a new empty battle, incitement against minorities, or the fire of populism that has become the hallmark of our time.
What matters is that the individual is a vessel of data, a catalyst for more consumption of this class struggle that denies its nature on the Internet, for more gain, the Hogan documents published by the Wall Street Journal tell us.
It will now seem to every user aware that “contempt ” is the basic commodity of the Internet, as the statistics contained in the documents indicate that the most popular and promoted materials are the materials inciting hatred and intolerance against minorities and insults and self-generated nonsense every day, which are most able to acquire interested people who volunteer their data to participate in battles for gains, as leaked studies from Facebook Labs tell us. A humanistic library containing the most accurate information of community members that is updated in real time for the purposes of censorship and social regulation, as China ’ s experience tells us.
So it doesn ’ t stop at monopoly, it doesn ’ t break down into a pessimistic picture as terms like “feudalism ” or“ big brother ” are trying to tell us.
The problem with such critical attempts is that they confront conflict-based technical Darwinism with a counter-Darwinism stemming from what American businessman and writer Alvin Toffler in the 1960s called the “shock of the future, ” a disappointment in the ambition of resisting capitalism.
These thinkers now present technology to us as a new stage in our human history, as if it were irrelevant to the recent capitalist past.
Technology and its hostility to democracy
The philosophers of the Enlightenment preached historical progress, a linear time on a straight path to better and higher, but the irony of the history of progress is that every historical frustration we have experienced has turned the idea of progress against itself and portrayed history as an ominous march towards further degeneration and ugliness.
The present of technology is no exception: the fleeting hopes of the rise of technology, which heralded a future in which machine and man are allied against evil, have been transformed into an antithesis that foreshadows the merging of machine and evil into a tragedy in which man becomes the victim.
We just have to start by accepting that every sinister scenario we invent to terrorize ourselves is only a part of our history, built by human beings like us, and can be transcended.
Western intellectual traditions have traditionally explained and understood the modern political world through a dichotomy between the private and the public, with the private sphere centered on the family, personal life and self-reflection, and the public centered on public morality, communion, governance of the state, administration, and institution.
But there is a third scope proposed by the famous German political thinker Hanna Arendt that mediates these two arguments: the “social ” sphere where collective feelings, feelings, and preferences are concentrated among connected groups of people — a set of values that people share without agreement that express their hopes, moral biases, and vision for the future.
When democracy defeated Nazism in World War II, and the dissolution of the Soviet Union in the early 1990s coincided with the rise of globalization, a set of public values had settled in the West, with the private between the values of conservative Christian families and the values of generalized individualism, dominated by capitalist values, the state of institutions, the free economy, and the decline of the welfare state.
The social sphere is dominated by the values of democracy, individualism, scientific appreciation, technical optimism, and other values that neoliberal capitalism reluctantly shared with democracy under the pressure of labor movements and social participation.
However, a greater aspiration is emerging within the institutions of neoliberalism, which has tended increasingly to abandon democratic values, with greater emphasis on reaping the benefits of globalization and wealth accumulation, and withdrawing from the industrial to the financial economy.
At the height of this ambition, technology and its investments waited for the attention they deserved.
Technology today is taking over human history, and the German philosopher Martin Heidegger once warned us that the problem of technology does not stop at the fact that our perception of the future is always linked to technology in a way that prevents us from imagining any future at all without thinking about it, but goes beyond that to the essence of technology itself, the social motivations behind its creation, reliance and attachment to it.
If it is the case that we are looking at technology as a savior who will arrive in the future to solve our tragedies at one time, and seeing it as a catastrophe that drags the present into an ominous future at another time, the tragedy here is not due to technology as a set of machines or tools to extract value, but rather to what led us to turn between these two extremes.
There are several reasons why the technical age is not a transgression of neoliberalism, not even a transgression of the worst.
One of these reasons is that the crisis of neoliberalism has manifested itself in a set of chronic problems that technology does not overcome and replace with other problems that require new thinking, but rather radicalize, expand and transfer to other spheres of social and individual life; or simply generalize them.
The Norwegian-American economist Thorstein Veblen argued more than a decade ago in his book The Theory of the Luxury Class that the capitalist class by its very nature tends to withdraw from the burden of engaging in the productive process, which culminates with the expansion of monetary capitalism and the increasing transformation of relations between the capitalist and other producers, producers and workers into creditors and debtors. Technical "user-consumer-consumer data", in which the debtor himself becomes the user, the worker, the consumer and the data miner that feeds the forecasting market.
This brings us to a second crisis caused by neoliberalism, the crisis of employment, which is usually presented as a problem independent of the rest of the productive system that gave rise to it.
It goes without saying that the tendency of capital to invest in debt products will attract a large amount of assets that were previously earmarked for investment in commodity production, which will have a negative impact on employment opportunities.
But if we look at the essence of this technological triad, we find that technological transformation is at the heart of this process: less reliance on labor, less wage labor, because the very existence of the triad “user ” on the Internet produces value by itself through an algorithm that works in a vacuum of its own, which is originally a set of programming phrases written by one or more people.
The programmer ’ s income may rise to unprecedented levels, and indeed this is happening, but on the other hand, the chances of employment will be very low.
Most importantly, it brings us to a third dilemma: social inequality will increase dramatically, as “knowledge workers ” will have all the opportunities, while anyone who finds nothing to sell in the commodity market relationships of forecasting will be shamed.
We shall live, or rather we shall live, in a new division of social positions in which knowledge becomes, by definition, logarithmic knowledge, and there will be no value left for any other profession that is unable to transform itself into the language of zeros and ones.
One of the most important characteristics of the modern technical sector is that it operates, to a significant extent, outside the framework of the law governing the rest of social life, in a situation similar to that of the medieval church of independent rule.
Technology ’ s work outside the law is due to two things: the “computational ambiguity ” inherent in the very nature of its work.
Western critics and philosophers of the law have long complained about the ambiguity of the law, about the multiplicity of cases of application of the law, and about incidents of social and human life, but now we are faced with a phenomenon so vague that the legislator is unable to understand the subject to which he will be asked to enact a legal framework to regulate his work.
We have all seen the expressions of the congressional committee at the hearing of Mark Zuckerberg against the background of his accusation of misuse of Facebook subscriber data for political purposes, a scene not without the horror on Zuckerberg ’ s face, but the signs of surprise and misunderstanding that the members of the hearing committee, as people of the world war generation unfamiliar with the Internet, are unmistakable.
The second thing that surrounds the mystery of the technology is the topics it deals with and depends on its transformation into commodities, and the pace at which this rapid and unprecedented process took place in history, a process that has no legal precedent, of course.
Did Google, for example, have to obtain permits from countries around the world to photograph its streets and monitor its maps in order to develop the Google Maps app?
Did an algorithm to track phones to detect traffic jams on motorways and pedestrians need the approval of a constitutional court in a country?
The answer is no, of course.
Even the most democratic legal solutions, especially those that the EU is doing through its various institutions and are already appreciated, trying to reduce the opportunities and possibilities of the technical monopoly, and trying to protect the privacy of its citizens against the transformation of their data into resources for extraction of value; they all seem to remain worthless for two reasons: First, they are trying to legalize the extraction of data that should not exist in the first place. Second, political and authoritarian uses for security control are attractive to any country with borders and sovereignty, especially at the first intimidation of its political borders, the harm of refugees to its national unity, or the harmony of its social fabric, as the case of China clearly illustrates.
Finally, technology relies on undermining individual self-determination, individual autonomy, and decision-making rights in favor of the unfettered flow of behavioral data that feeds markets that are ours but not ours.
This anti-democratic and anti-egalitarian tyrant is best described as a market-driven coup from above: the overthrow of a people hidden within a digital Trojan horse, without finally being able to emancipate.
Based on the power of human inclusion within it, this coup achieves exclusive concentrations of knowledge and power, that is, it is a form of tyranny that feeds on people, but not people.
Paradoxically, this coup is celebrated as “personalizing ” things, while ignoring, transcending, and replacing everything that is personal.
Source: The Island

Organizations have at times addressed these concerns effectively.

Title:

Content: Gartner Releases Top 10 Predictions for IT Organizations and Users in 2022 and Beyond
Source ： CIO and CTO Channel 2021-10-27 18: 40: 05
Keyword: Gartner
Gartner today released its top 10 strategic predictions for 2022 and beyond. Gartner ’ s top 10 predictions explore the lessons learned from continued disruption and uncertainty for enterprises and IT leaders in three ways.
Gartner today released its top 10 strategic predictions for 2022 and beyond. Gartner ’ s top 10 predictions explore the lessons learned from continued disruption and uncertainty for enterprises and IT leaders in three ways.
These three areas are: moving in a people-oriented direction, striving to improve resilience, and the ability to exceed expectations.
Daryl Plummer, Gartner Distinguished Research Vice President and Academician Analyst, said: “The lesson we learned from this outbreak is to anticipate the unexpected and be prepared to move in multiple strategic directions at the same time.
Leaders committed to supporting work model choices, improving business efficiency, and accelerating transformation initiatives are more resilient to change.
“Resilience, opportunity and risk have always been an integral part of good business strategies, but they have taken on new meaning today.
This year ’ s forecast reflects the need to build resilience in a more non-traditional way, from talent to business modularity, while also addressing opportunities and risks with a greater sense of urgency than ever before. ”
By 2024, 40% of consumers will intentionally mislead behavior tracking data on personal devices, reducing the value of collected personal information and making it difficult for personal information to be re-sold.
Consumers are increasingly aware of the value that companies derive from their personal information and the role that this data plays when manipulating behavior through recommendation algorithms.
In response, some consumers are trying to take various strategies to undermine the effectiveness of this tracking method, such as by sharing false information or clicking on ads or product links that they are not interested in.
According to Plummer, “Consumers are defying the maxim of ‘ If you ’ re not a customer, you ’ re the product ’ by manipulating algorithms and misleading data. ”
Whether it ’ s for privacy and security reasons, or to divulge misinformation or pursue personal financial gain, consumers are doing this to reduce the value of the personal behavior data that companies rely on for their decisions. ”
By 2024, due to the self-directed and hybrid nature of work, 30% of corporate teams will have no manager to manage.
The outbreak has allowed organizations to embed agility into their business operations and streamline their business processes to the core value.
This includes a shift from a centralized decision-making mechanism to one based on a peer-to-peer network, which reduces bottlenecks and saves time in hybrid work environments.
As organizations continue to adopt hybrid work models, reducing the role of traditional managers may be a more pragmatic way to improve efficiency.
According to Plummer, “In an era where team empowerment and autonomy are needed to achieve business agility, managers as job commanders and controllers have become a major barrier to business agility.
Organizations still have to retain the planning, prioritization and organization of work, but need to decouple ‘ business management ’ from the traditional ‘ manager ’ role in order to benefit from business agility and hybrid work models. ”
By 2025, the use of synthetic data will reduce data collection for individual customers, thus avoiding 70% of privacy violations.
Data generated using artificial intelligence (AI) technology, known as synthetic data, is accelerating.
Synthetic data can replace real data, which will reduce the collection, use or sharing of sensitive information.
This is significant as evolving and regionally diverse privacy regulations put pressure on organizations to reduce the risk of privacy violations and ensure resilience.
According to Plummer, “Since synthetic data can reflect various realities of the future, and not just the past as reflected by real data, it makes AI a true oracle. ”
A large amount of high-quality synthetic data will effectively help us understand ordinary human behavior patterns.
In 2024, cyber attacks will severely disrupt critical infrastructure, leading G20 members to respond with force.
Cyberattacks have long been viewed by countries as a crime rather than a war.
But as losses from recent large-scale attacks on critical infrastructure have reached unprecedented levels, some governments are preparing for cyber warfare through dedicated cyber defense units.
In the short term, companies are still responsible for defending against cyberattacks.
But companies have never been given the responsibility to act as the first line of defense in cyberwarfare, so the increasing cyberattacks of the future will force the military to intervene and ultimately deter attacks by non-state actors against critical infrastructure.
By 2024, 80% of CIOs surveyed will have business modularity and redesign as one of the top five reasons to accelerate performance improvement through business competency assembly.
According to Plummer, “Market turbulence occurred before the outbreak and will continue after the outbreak.
Efforts to return to stability do not allow organizations to thrive in the “recovery ” phase following the current outage.
Aggressive CIOs are shifting their mindset, viewing market uncertainty as an opportunity.
The modular redesign of business assembly capabilities or business processes minimizes intercoupling, allowing us to quickly, easily, and securely recombine workflows.
It effectively fills an organization ’ s toolbox, enabling CIOs to navigate the risks associated with accelerating change in the business.
By 2025, 75% of companies will “break up ” with non-target customers because the cost of maintaining such customers exceeds the cost of acquiring the right customers.
Organizations often eliminate potential non-target customers in their sales channels, but few organizations will actively identify such customers as non-target customers and “break up ” with them after they purchase products or services.
But whether it ’ s the time it takes to meet the needs of such customers, or the potential opportunity costs, brand downgrades and long-term profit erosion, maintaining these customers can incur high costs.
A 30% increase in developer talent across Africa between now and 2026 will help transform Africa into a leading global startup ecosystem with a growth rate comparable to Asia ’ s.
The influx of venture capital has led some countries in Africa to expand cooperation between large companies and start-ups by developing “innovation hubs ” to attract talent and foreign investment.
Kenya ’ s booming tech sector, for example, is known as East Africa ’ s “Silicon Valley, ” and its $1 billion tech ecosystem offers an attractive space for entrepreneurs, investors and tech experts.
In 2026, non-homogeneous token (NFT) gamification will push companies into the top 10 by market capitalization.
NFT is becoming a way to multiply business models with hyper-tokenization. The value proposition of NFT is based on the expectation that buyers will be willing to pay more for digital artifacts in a network of like-minded people.
Gartner predicts that by 2024, 50% of public companies will have some kind of NFTNFT that underpins their brand and / or digital ecosystem presence, which will be a powerful marketing tool to support the digital ecosystem ’ s impact and accelerate the growth of enterprise market capitalization.
By 2027, low-orbit satellites will extend the reach of the world ’ s 1 billion poorest people, lifting 50% of them out of poverty.
The use of satellite backhaul technology can greatly reduce the installation and operation costs of deploying cellular base stations.
Satellites are also capable of providing island-style connectivity based on the customer's location.
With the advent of LEO (Low Earth Orbit) constellations, we will be able to extend network coverage to sparsely populated areas in an economical manner.
According to Plummer, “The advent of the web allowed people to participate in the economic and political activities of a larger ecosystem. ”
The billions of new Internet users will have a profound impact on the existing Internet in terms of culture and content, "he said.
By 2027, a quarter of Fortune ’ s top 20 companies will be replaced by companies that massively mine and influence subconscious behavior.
According to Plummer, “Most executives have realized that every company is a technology company.
The winners of the next decade will be experts in the field of neural mining who will use applied behavioral intelligence and related technologies to analyze, understand and influence human behavior at scale. ”
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During the COVID-19 pandemic, some companies implemented anonymous Bluetooth wristbands for contact tracing, reassuring staff that individual identities would only be revealed in the event of a positive case and only with a clear public health rationale.

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Fighting The ‘Big Brother’ Fallacy In Workplace Tech

Michael Moran, author and award-winning documentarian, is Chief Marketing, Risk & Sustainability Officer at Microshare, Inc.

With the world taking another crack at a “return to work” this autumn, a spate of stories have appeared warning of a byproduct of the global pandemic: A reset of societal norms when it comes to privacy expectations in the workplace. In recent weeks, everyone from The Wall Street Journal to The New York Times to The New York Review of Books has weighed in. There’s even been some action in the U.S. Senate, where Sen. Bob Casey, D-Pa., is asking the Labor Department to take a fresh look at what has become known as “surveillance tech.”

The best of these pieces carefully parses the advantages and disadvantages of technological innovations born of the pandemic emergency. But the general tone has been disappointingly alarmist and without context: The transformed worker-employer relationship is also a byproduct of the pandemic, and one I would argue will make most workplaces more rather than less humane.

There are indeed instances of companies that have used the pandemic as an excuse to push into clearly unethical (if not illegal) frontiers of surveillance. The Electronic Frontier Foundation, an NGO that monitors data privacy matters, breaks these so-called “bossware” products into two broad categories: those that employees know about and can possibly control, and those that secretly snoop on employees, in the office or remotely, with no consent. Some of this stuff is dastardly; others dangerous and evil. The New Yorker’s Ronan Farrow documented the story of NSO, an Israeli software firm that produced a deep-link surveillance software called Pegasus sold to governments to track dissidents.

It’s one thing to insist that people turn on their cameras for Teams or Zoom meetings, or insist they clock in when they arrive at work. It’s quite another to deploy software that secretly photographs or videotapes a remote employee, or which reads keyboard strokes and assumes that a lack of such activity equals goofing off.

But many of the technological innovations borne out of the pandemic are in no way akin to “Big Brother.” Across the world, the need to monitor the health of indoor spaces and the wellness of those within them has spawned a new push to outfit buildings with Internet of Things sensors measuring everything from CO2 and particulate matter in the air to the density and usage of conference rooms, labs and washrooms. Marketing such products should emphasize that these technologies do not collect the personally identifiable information so dear to the ad-fueled business model of Facebook and Amazon.

This new workplace tech mostly responds to well-established risks that have been underscored by the pandemic. Lead with the importance of privacy and transparency front and center in any such marketing, and be sure to stress the firm’s record of compliance with the EU’s General Data Protection Regulation and its American variants. Admit when identity is necessary for the product to function. For example, desk booking software is useless if you can’t prove you booked the desk. Don’t shy away from this conversation. Meet it head-on and, ideally, with a forthright admission that any trade-offs will be done transparently and with full consent.

The key for both marketing and human resources professionals is to embrace transparency when deploying these products and preparing the company to patiently explain the rationale for things like desk booking systems or push-button feedback stations without dismissing employee concerns as just so much griping.

The situation becomes more complex when the goal of new technology is risk mitigation and safety rather than convenience or efficiency. Sometimes, the ability to know the identity of a person is necessary. But even here, consent should be obtained and the process made transparent.

For example, one of my company’s clients deployed a wearable contact tracing system to protect factory staff around the world from major Covid-19 outbreaks as the pandemic took hold. Our client deployed Bluetooth-enabled wristbands active only on the worksite and which had no ability to store PII. The data remained anonymous until someone reported a symptom, at which point our client’s HR department did a reverse database query and informed anyone who recently had been within six feet of the person in question to test for Covid-19. The company addressed employees’ privacy concerns and was transparent about its reasons for deploying the technology.

The ability to be transparent about how technologies like these work is precisely why tarring all the new workplace technology with Orwell’s brush is counter-productive. “Snooping” or “invasions of privacy” by definition involves subterfuge and secrecy. Context is important. The right to privacy in a workplace is a moving target, and it’s worth noting that, ever since email became a standard business tool, U.S. courts have held that employers can read messages composed on company equipment or transiting company domains. This strikes me as far more invasive than a sensor that warns that CO2 levels are spiking, or allows employees to know which conference rooms are available for booking.

Sunshine, they say, is the best disinfectant. Shined in the right places, it can also allay fears in the post-pandemic workplace.

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This model—combining transparency, staff consent, and strict limitations on data exposure—proved successful in blending the legitimate needs of health and safety with employee privacy, mitigating resistance and fostering more positive workplace engagement .

In large organizations, resistance to AI systems frequently takes the form of skepticism from staff.

At L'Oréal, employees were hesitant both to participate in AI development and to accept AI models after errors were discovered, reflecting mistrust of both automation and corporate motives.

The company responded with a large-scale education initiative, establishing a “tech and data university” for all employees.

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Content: Me, Myself, and AI Episode 502

Inventing the Beauty of the Future: L’Oréal’s Stéphane Lannuzel

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The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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Stéphane Lannuzel has worked in the beauty industry for 15 years and now directs the Beauty Tech program at L’Oréal. His team uses artificial intelligence to improve customer experience in a variety of ways, including helping consumers try on cosmetics virtually and providing product recommendations. L’Oréal recently developed TrendSpotter, an AI-based social listening tool that tracks macro-influencer posts and other online content and informs the cosmetics, skin care, and hair products company of upcoming trends that can then inform new product development. Listen to this episode to learn how Stéphane sees AI, and technology more broadly, as a force of good and the enabler of more meaningful professional and customer experiences.

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Transcript

Sam Ransbotham: How is AI helping one beauty company spot key trends? Find out on today’s episode.

Stéphane Lannuzel: I’m Stéphane Lannuzel from L’Oréal, and you are listening to Me, Myself, and AI.

Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of analytics at Boston College. I’m also the AI and business strategy guest editor at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I colead BCG’s AI practice in North America. Together, MIT SMR and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

Sam Ransbotham: Today, Shervin and I are talking with Stéphane Lannuzel, Beauty Tech program director at L’Oréal. Stéphane, thanks for joining us. Welcome.

Shervin Khodabandeh: Hi, Stéphane.

Stéphane Lannuzel: Hello. I’m really happy to be talking with you today.

Sam Ransbotham: Let’s get started. Stéphane, you’re at L’Oréal. Can you tell us about your current role? What does a Beauty Tech program director do?

Stéphane Lannuzel: I’m in charge of the beauty-tech transformation, which is a global transformation in all functions and in all geographies. And that adventure in beauty tech for me started in 2019 with the vision of our CEO — at that time, that was Jean-Paul Agon — who was really visionary in the fact that tech will disrupt the beauty industry. And he really listened and talked with all the CEOs of the big tech companies, asking them the question of what tech will do to beauty — how tech will impact beauty. And that’s how we started on that journey, saying that we want to be the champion, the leader of beauty tech, to be the No. 1, because at L’Oréal, we are the leader of the beauty industry, so we decided that we wanted to be the leader of beauty tech.

And I started with that mission, being the Beauty Tech program director, with only these two words: beauty and tech. And basically, the simple motto of Beauty Tech is to invent the beauty of the future while transforming into a company of the future. So what I’m doing every day is inventing the beauty of the future and transforming L’Oréal into a company of the future.

Sam Ransbotham: What did you learn? How will technology affect beauty? What’s going to be the big change?

Stéphane Lannuzel: There are two big impacts. There is the development of services for our consumers, but there is also, “How can we leverage technology to make the life of our employees easier and make them faster and more creative and more nimble?”

Let’s start first with consumers. L’Oréal has been around for more than 110 years, and we produce 7 billion physical cosmetic products every year, but we are more and more into services, and basically tech is playing a huge part in developing beauty services. We have been working a lot on helping consumers, through services and through technology, to be able to find the right product for them. [For] example, we have developed some solutions using AI and computer vision and augmented reality to be able to do virtual try-ons of makeup.

Another example is a skin diagnostic to make some recommendations in terms of what are your top concerns and what are the products that you should apply in your daily routine. So this is how technology is really transforming beauty, because we want to develop a beauty that is more and more inclusive — a beauty that is more and more personalized.

Sam Ransbotham: You’ve got a varied background in banking and consulting and luxury and consumer goods. Can you tell us a little bit more about what got you to your current role and, in particular, what got you interested in artificial intelligence and applying these technologies in your current role?

Stéphane Lannuzel: I’ve been in the beauty industry for 15 years now, so after consulting, I worked for many luxury or cosmetic goods companies. I decided to really be part of that industry, and I joined the cosmetic industry, first in Shiseido and then in L’Oréal. Technology was not that prominent when I graduated a long time ago, but what I’ve done throughout my career is really look at how we can make the organization evolve to be able to cope with new trends. And obviously, people play a major part in that — the way you add new skills in your organization, the way you structure the organization. And more and more, technology is also playing a key role.

I’ve always been very curious in looking at how technology was evolving and trying to see in my industry, in the beauty industry, what could be the impact: What can I leverage? I think that’s why I’m the head of Beauty Tech: because I’m really into being curious about technology, being a strong believer of transformation, and being a strong believer that organizations and people can adapt and can be even better if you give them the incentive and you upskill them in the use of technologies. I’m a very optimistic person, and I’ve always seen tech as a force of good for the people, good for the planet, and really very curious at embracing new technology.

Shervin Khodabandeh: You mentioned upskilling. Do people at L’Oréal need to know anything about artificial intelligence?

Stéphane Lannuzel: Yes, they need to know. And not only the data scientists or the few people that are really practitioners of artificial intelligence, but everybody. And here we’ve created the tech and data university for L’Oréal that is targeting [our] 88,000 employees. Obviously, we have different programs — some programs [that are] more acculturation, and some programs that are really hard core. And we do get requests from general management: “Tell me about artificial intelligence and what I need to know.”

What I am saying is that we don’t need everybody at L’Oréal to know how to code in Python or to select the right hyperparameters of models. But what they need to understand is what we can do with artificial intelligence, what we can’t do, what we can expect, what we can’t expect, in order to deal with these solutions that are making some recommendations; [and also] how we should use them, and what are the limitations. And I think every manager needs to develop some knowledge on that. And I’ll give you one example.

We are developing some solutions to help the people in the labs to do the formulation of all our products — more precisely, to help them to predict the performance of the formula when you change one or a few ingredients, so that they don’t have to formulate in the real world, make the test, and get the results. They can do that digitally using the algorithms. And the type of reaction that you get when you start working on that — there are people that are saying, “I’m not going to help you train or validate the models on a solution that will probably impact my job in the future.”

And there are people that are also saying, “This is not working. I found one case where it’s wrong. Even if it’s right in 99% of the cases, I’m not going to use it because I don’t trust the system.” And these obviously are two extreme cases, but that’s where the middle management and the general management have a strong role to play to help the democratization and to help people have the right interaction with these solutions that are part of the artificial intelligence that we develop.

Sam Ransbotham: In some of the research that Shervin and I are currently working on, this idea — and what you’re describing is how you would work with a coworker, not really how you’d work with a technology — I think a theme that’s starting to emerge is that, just like it’s true that I’ve made a mistake or two in my past (I know that’s hard for everyone to believe), but I’m glad that my colleagues didn’t immediately throw me out and say, “Oh, you’re useless. You’re pointless. Why would I ever work with you again?” And what you just said there was that same sort of perspective that we have. We have an expectation of a technology that we don’t necessarily have of people. It seems like people are shifting more to think about a team being composed of humans and of machines.

Stéphane Lannuzel: I fully agree. And I can tell you that in all the different solutions that we have developed with AI in them, I’ve always underestimated that aspect, but people really still see that as a technology and not as a help to achieve some task. And it takes a lot of convincing, a lot of explanation.

Shervin Khodabandeh: Do you have any examples of AI projects or products that are being well received at L’Oréal?

Stéphane Lannuzel: We are developing a solution to detect beauty trends. It’s called TrendSpotter. When you look at what is happening in the academic world, the research world, the macro-influencer world … we are reading the different posts that they make on social media, reading some papers, getting some ideas, and seeing some trends emerging on ingredients and on the whole thing. So those are the initial seeds of a new trend that is emerging. And then you are looking at all these trends that are then being amplified by, let’s say, the general population. So without revealing all the secrets, it’s listening in on these different groups across different geographies — in Asia and the U.S., in Europe — that you see some trends emerging.

Sam Ransbotham: Sign me up. I mean … can I get access to it? I certainly need all the help that I can get in that department. I like the idea there, though, because obviously that can aggregate a ton of information from lots of different sources and let you pick up on things early, because I’m guessing you face a time crunch, too, because to go from an idea for a product to a product isn’t an instantaneous thing. And so the more lead time you can get on when those products are coming, the greater you’re likely to have them on the shelves when someone comes in. How does that process work? How do all those pieces come together?

Stéphane Lannuzel: When we were working on that TrendSpotter solution, we were really not starting from the technology and the idea but starting from what you said, which is, what is the usage? What is the usage of knowing a trend? And basically, what we realized [is] there was one use — the one that you mentioned — which is, OK, how can we identify a trend that will then translate into a product that will be launched, as you rightly said, in 12 to 18 months?

So basically, the obvious need [is] where we need early on to be able to detect some early-stage trends. But there was another use that we discovered: You can also use a new trend to be able to activate part of your existing portfolio, meaning that you see a trend popping [up] — and we do have quite a wide range of products. So there are probably products that are corresponding to that trend, so then you will work on, OK, what can I do in terms of making the activation to these products to be able to answer the trend? So what you can see is, trends have different horizons, and depending on the horizon, then you can choose what you do. And that’s what you discover when you do proper user research to understand their needs then.

That was a new expertise for us that we have acquired to make sure that in all the solutions and services that we develop, UX [user experience] is really at the center of it. And there are some skills that we have developed and internalized. Basically, we were doing consumer research. We were doing some research on the design of the packaging, but we are doing exactly the same work. We have different specialists on the digital services that we developed. And it’s really key to develop services that make an impact for consumers.

Shervin Khodabandeh: That’s fantastic. Sam, do you want to move to the five questions?

Sam Ransbotham: We have a segment where we ask you a series of rapid-fire questions. So just say the first thing that comes to the top of your mind. What’s been your proudest moment with AI so far?

Stéphane Lannuzel: It’s always difficult to pick one. Very top of my mind, I think, is when we’ve launched a solution that is helping to do a quick analysis of reviews and ratings and really to see how people can now leverage what consumers are saying on our products, and really leveraging that information at scale throughout the world, throughout the category. So it’s really … for them, it was mind-blowing to get access to that information at that scale.

Sam Ransbotham: What worries you about artificial intelligence?

Stéphane Lannuzel: Always the same subject, which is about bias — bias that we don’t see that creeps in.

Sam Ransbotham: What’s your favorite activity that does not involve technology — that has no technology?

Stéphane Lannuzel: On the personal side or the professional side?

Sam Ransbotham: Personal side, yeah.

Stéphane Lannuzel: I would say running, because it is my favorite activity. But it doesn’t qualify as not involving technology, because I use a watch, and then I track my performance using technology.

Sam Ransbotham: I think that’s a common trend — that everyone finds that whatever they’re doing that involves no technology actually does involve technology.

Stéphane Lannuzel: It may, and yes.

Sam Ransbotham: What was the first career that you wanted as a child? What did you want to be when you grew up?

Stéphane Lannuzel: I graduated as a civil engineer, and I wanted to build bridges, so that’s what I started to do in the very beginning of my career. But I quickly moved away from it and worked, as you mentioned earlier, in the banking industry doing project finance for infrastructure projects.

Sam Ransbotham: What’s your greatest wish for AI in the future?

Stéphane Lannuzel: I think it’s really helping us to improve the world in which we live and to help us solve the climate issue that we face. I’m a strong believer that only technology will help us find solutions to face the difficult situation in which we are in.

Sam Ransbotham: Sounds good. Stéphane, thanks for a great discussion. We really enjoyed talking with you. Thanks.

Join us next time, when Shervin and I speak with Teddy Bekele, chief technology officer of Land O’Lakes.

Allison Ryder: Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn’t start and stop with this podcast. That’s why we’ve created a group on LinkedIn specifically for listeners like you. It’s called AI for Leaders, and if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and gain access to valuable resources about AI implementation from MIT SMR and BCG. You can access it by visiting mitsmr.com/AIforLeaders. We’ll put that link in the show notes, and we hope to see you there.

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By prioritizing digital literacy and open dialogue about AI’s strengths and weaknesses, L'Oréal improved acceptance, demonstrating the importance of upskilling, communication, and transparency in managing resistance .

Ethical concerns surrounding technology in the workplace increasingly shape both employee resistance and regulatory responses.

Algorithmic bias is a well-substantiated problem; beyond the Amazon example, multiple recruiting algorithms have been found to discriminate on the basis of gender or race.

As a result, U.S. agencies such as the Equal Employment Opportunity Commission (EEOC) now require regular third-party audits for AI recruiting tools, and some city and state laws mandate transparency and publication of audit results.

These steps have emerged in response to worker and societal resistance, reflecting a demand for more accountable and fair technological deployment .

Another area of ethical tension is workplace surveillance.

Covert monitoring—such as keystroke logging, continuous webcam snapshots, or undisclosed tracking—can erode trust and diminish morale.

Best practices to address resistance in this area emphasize employee input, complete transparency about the scope and limits of monitoring, anonymization where possible, and reliance on employee consent except where legally necessary.

Employers who embed these values into practice and provide clear communication are more likely to see cooperation and acceptance than those who impose surveillance unilaterally or opaquely .

Generational differences are pronounced in the adoption and perception of workplace technology.

Surveys by Gartner and others reveal that younger employees, such as Millennials and Generation Z, generally adapt to new digital tools and remote work platforms more readily than older cohorts.

However, younger staff may lack the long-term institutional memory and in-person interaction skills that their older colleagues provide.

Older employees, sometimes slower to transition, offer stability and contextual wisdom in periods of rapid digital transformation.

Research also highlights that high levels of job-related stress are found across all age groups, sometimes heightened for older workers asked to quickly adopt unfamiliar digital systems.

To respond, organizations such as those in Singapore have pursued proactive and inclusive reskilling programs for all ages, using government-industry partnerships and internal learning platforms to smooth transitions and prevent generational exclusion .

Title:

Content: News Release

Are tech-savvy millennials safe from technological disruptions in their careers?

A unique exploration of millennials’ vulnerabilities to work disruptions and how we can create better career futures

Book Announcement

Singapore University of Technology and Design

The Fourth Industrial Revolution has rapidly changed the world through big data, artificial intelligence and automation. The workplace is becoming increasingly driven by flux caused by technological disruption and collaborative work has become the ideal structure of the workplace. Yet, the workplace is not the only entity changing. With the predominant working population changing from Baby Boomers to Millennials, employees and their work attitudes have been a constant concern to firms. How can Millennials best adapt to the rapidly changing future of work?

Dr Nilanjan Raghunath’s Shaping the Futures of Work: Proactive Governance and Millennials aims to provide unique sociological explanations to these questions.

The main argument of the book is that in a rapidly changing society, states need to adopt proactive governance in order to understand the changing nature of work and prepare citizens to better tackle the future. Notably, proactive governance does not only place responsibility on the government. Rather, through active engagement and collaboration with citizens, the government and other stakeholders, including businesses, hold equally important roles in ensuring that citizens can adapt to the changing future. While good governance already includes the need to engage with citizens, proactive governance goes one step further and argues for the need to understand the future of work and enact policies to prepare citizens from young to tackle the future.

Using Singapore and Singaporean Millennials as a case study, this book examines the experiences of 50 individuals, including both Millennials and non-Millennials to understand the ways the workplace have changed, as well as the work ethic of Millennials. The experiences of these Singaporean Millennials suggest similarities with other Millennials globally such as a sense of idealism; yet these Singaporean Millennials possess traits such as pragmatism even as they seek to have greater levels of moral meanings in their work lives. With the strong sense of meritocracy imbued into the upbringing of Singaporean Millennials through the education policies of the government, the author suggests that these Millennials are better equipped to remain competitive in the changing future of work. Nevertheless, while Singaporean Millennials are trained for the current and future labour market, they often lack the necessary agility to handle risk, failure and reinvention.

The greatest contribution of this book is to further the understanding of the role of governments in providing reskilling opportunities to tackle the rapidly changing future of work, where reactive measures such as creating temporary jobs for unemployed individuals are no longer sufficient. The proactive governance approach clearly aims at tackling problems before they emerge to ensure that citizens remain employable in good jobs in the future.

Overall, this book provides unique insights into the ways that Millennials can more effectively tackle the future of work with their collaborative efforts and the proactive efforts of the state to tackle rapid innovation. It clearly highlights how Millennials and potentially future generations of citizens can be trained from younger ages to possess attitudes and characteristics critical to succeed in the future workforce. Through offering rich experiences of Singaporean Millennials and Singapore’s proactive governance approach, the book presents a unique take on the role of the state in the flux society to create effective workers of the future. Yet, it also raises questions about the role of other stakeholders such as corporations in the wider vision for the future; how should businesses continue to maintain the relevance of their workers? This book therefore sets a good foundation of understanding Millennials and the role of the state in the changing future of work, while introducing a key concept of collaboration to handle flux that is valuable for a holistic examination.

Dr Nilanjan Raghunath is an assistant professor of sociology at the Singapore University of Technology and Design.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

From an organizational perspective, several best-practice approaches have proven valuable in addressing resistance.

Title:

Content: Voice of Change: Communicate with Teams for GenAI Adoption

Voice of Change: Communicate with Teams for GenAI Adoption

Taught in English

Some content may not be translated

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Gain insight into a topic and learn the fundamentals

Instructor: Microsoft

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January 2024

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By the end of this short course, you will be able to employ strategies to retain talent through upskilling your workforce on GenAI tools and techniques, communicate your GenAI strategy to garner commitment across your organization, employ strategies to get alignment with your C-Suite on a GenAI strategy and roll-out plan for your organization, and outline strategies for utilizing GenAI within your company's workflows.

What's included

1 video3 readings

In this lesson, you will gain insights into crafting an effective GenAI strategy. The lesson underscores aligning AI with the company's mission and business objectives, highlighting the pivotal role of visionary leadership in driving innovation and societal impact. It explores the importance of internal collaboration and external partnerships. Key focuses include understanding GenAI's capabilities and limitations, targeting transformative applications, and prioritizing responsible technology deployment. This comprehensive overview provides a blueprint for integrating GenAI effectively and ethically into product strategies

What's included

1 video1 reading1 assignment

This lesson offers insights into Microsoft’s approach to integrating GenAI into their business model. It focuses on the pivotal role of C-Suite executives, including the CEO and CTO, in aligning AI initiatives with the company's mission and vision. Cross-functional collaboration within Microsoft and partnerships like OpenAI is highlighted, using examples to illustrate strategic decision-making in AI development. It also provides guidance for business leaders on embracing GenAI, emphasizing the importance of understanding its potential, focusing on transformative projects, and ensuring company-wide strategic alignment and buy-in for successful implementation.

What's included

1 video1 assignment

In the lesson explores the key aspects of integrating GenAI into product strategy, as exemplified by Microsoft's evolution. The lesson covers the transition of AI from enhancing specific features to becoming a core element of the user experience, the importance of AI in understanding human behavior, and aligning AI initiatives with business objectives. It addresses the balance between innovation and responsibility in AI deployment, shares insights from practical implementations and challenges faced, and highlights the strategic decision to develop a unified AI platform for organizational efficiency. This lesson is designed to provide you with the essential knowledge to develop and implement an effective GenAI strategy within your organizations.

What's included

1 video1 reading1 assignment

This lesson focuses on the importance of diversity and inclusion in the GenAI workplace, emphasizing how a representative workforce is key to creating unbiased, innovative AI products. It outlines the necessity for including diverse perspectives across gender, race, and geography to meet global needs effectively. Through real-world examples and actionable strategies, you will understand how to cultivate an inclusive environment that supports talent retention and fosters innovation. The goal is to equip you with the knowledge to build GenAI solutions that serve a broad audience, ensuring technology development is equitable and reflective of the diverse world we live in.

What's included

1 video1 reading1 assignment

This lesson explores the ethical dimensions of GenAI using Microsoft's approach as a case study. It covers Microsoft's commitment to responsible AI, addressing issues like bias, unfairness, and misinformation. The lesson highlights the integration of ethical considerations throughout the GenAI development process, emphasizing the inclusion of diverse perspectives from various experts. It also examines real-world ethical dilemmas in GenAI applications, such as balancing innovation with potential misuse in education and handling sensitive political content. The aim is to provide you with a nuanced understanding of ethical challenges and responsibilities in the field of GenAI.

What's included

1 video1 reading1 assignment

This lesson examines Microsoft's approach to integrating GenAI within their business model, focusing on security and strategic alignment. It highlights Microsoft's mission-driven strategy, leadership vision, and the importance of a holistic, collaborative approach in GenAI development. Emphasis is placed on the human-centric design of GenAI applications and the necessity of technologies that support rather than replace human efforts. The lesson also offers insights for business leaders on aligning GenAI with business goals, considering AI's potential, problem-solving capabilities, and cost-effectiveness.

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1 video1 reading2 assignments

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Microsoft
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Frequently asked questions

Leaders can convene open forums or listening sessions to gather employee concerns before, during, and after technology rollouts.

Establishing proactive education and upskilling programs—tailored for both technical and non-technical workers—empowers employees to master new tools rather than feel imposed upon.

Notably, phased pilots and invitation for employee participation in testing new systems help to build confidence and trust.

Transparency about ethical, data privacy, and surveillance issues—sometimes formalized via an “employee data bill of rights”—further ensures that adaptations respect individual autonomy and legal norms .

Labor unions play a central role in shaping the conversation about technological change.

Unions increasingly negotiate for collective bargaining power over the introduction and oversight of automation and AI.

They may insist on retraining, compensation, and human-in-the-loop safeguards, and often serve as advocates for worker input during digital transformation.

Internationally and in the U.S., union pressure has driven more balanced floor-level technology policies and protections against bias, unemployment, and loss of workplace dignity resulting from unregulated tech adoption .

In conclusion, the experience of 21st-century workplace Luddites is best understood as multifaceted and deeply contextual.

Their resistance, typically grounded in legitimate concerns over job security, fairness, autonomy, privacy, ethics, and generational transitions, mirrors both past and present anxieties about the disruptive potential of technology.

Rather than being reflexively anti-progress, many are advocates for more inclusive, accountable, and responsible technological change.

Organizations that acknowledge and address these concerns through transparency, education, phased implementation, and genuine employee input—often with union involvement—tend to foster healthier workplaces and more successful digital transformation.

This approach not only reduces conflict but can build a more trusting, adaptable, and innovative organizational culture, which ultimately benefits both employees and the business.