Immortal AI

Category: Work & Power

Algorithmic management, hiring, layoffs, surveillance, automation and AI-mediated workplace decisions.

  • Meta Says AI Did Not Choose the Workers. The Accountability Question Moves One Step Back.

    Meta Says AI Did Not Choose the Workers. The Accountability Question Moves One Step Back.

    NEWS & ANALYSIS | WORK & POWER

    Meta has now given a sworn account of how it selected workers for its May 2026 layoffs. The company says AI played no part. That answer matters. It also shows why accountability cannot stop with the final person who applies a rule.

    By Andrew McDonald · Immortal AI

    Meta’s answer is categorical.

    In a sworn declaration filed in a California federal court on 23 July 2026, a senior human-resources director said AI did not select anyone for termination, assist decision-makers, calculate performance scores, rank employees or influence the criteria used in Meta’s May reduction in force.

    That is evidence filed in court by a named company official under penalty of perjury. It deserves to be reported plainly.

    It does not, however, resolve the dispute.

    Twenty-six workers allege that Meta’s process disadvantaged people who had disabilities, sought workplace accommodations or took protected medical, pregnancy, parental or family leave. Meta denies those allegations. The court has not decided who is right. The workers’ underlying claims are expected to proceed in private arbitration.

    The important question has therefore moved. If AI did not make the decisions, how should the people, criteria and records behind them be tested?

    What Meta says happened

    Meta says the layoffs were a structural reorganisation driven by business needs. According to the declaration of Linh Doan, Meta’s Director of Human Resources Business Partner Enablement, the company divided employees into decisional units and smaller cohorts. Business leaders documented why each group needed to change, then selected from permitted criteria tied to that rationale.

    Those criteria could include job level, historical performance, the most recent performance rating, tenure, location, job function, specialised skills and what Meta calls “spans and layers”: the number of people reporting to a manager and the number of reporting levels between an employee and Mark Zuckerberg.

    Doan said decision-makers could not use leave history, disability, accommodation requests or other protected characteristics. She also said they could not see workers’ leave information during the selection process.

    The declaration states that Meta’s performance ratings were assigned by managers without AI-assisted scoring or ranking. It also denies that AI-token usage, AI-adoption metrics, a “second brain” system or activity-monitoring data formed part of the selection criteria.

    For the four plaintiffs whose work visas gave the court particular concern, Meta provided individual explanations. It said two were selected using job level and performance, one because the required skills had changed, and one because of a performance rating assigned before the health condition and leave request at issue.

    These are Meta’s sworn claims. They have not been tested through a final hearing or full discovery.

    What the court actually decided

    US District Judge William Orrick refused to stop the layoffs. That ruling did not decide that Meta’s process was lawful. It did not establish that AI was used. It did not establish that AI was not used.

    The judge refused emergency relief because the workers had not shown the irreparable harm required for a temporary restraining order. The merits of their discrimination and retaliation allegations were not resolved.

    Orrick also required Meta to explain why four visa-sponsored plaintiffs were selected. Meta’s 23 July declaration was filed in response.

    The workers’ lawyers argue that the records needed to test Meta’s account are controlled by the company. Meta says its documented criteria and review process show that decisions were made by people for legitimate business reasons. That contest remains open.

    A person can make a decision inside a system

    The phrase “people made the decision” can be accurate and still incomplete.

    A person may decide which criteria are permitted. Another may apply them. A manager may have created an earlier performance rating. Human-resources staff may review whether the rules were followed. Software may store, organise or display the information.

    Responsibility does not disappear simply because no model produces a final name.

    The relevant chain is who designed the process; who chose the comparison groups and criteria; where the performance records came from; whether protected absences could affect those records; who checked for unequal outcomes; and what authority reviewers had to correct a problem.

    Meta says its criteria were neutral, fixed in advance and applied consistently. That is meaningful evidence of process discipline.

    But consistency is not the same as fairness in every case. A rule can be applied uniformly and still create an unequal effect if the underlying measure disadvantages a protected group. That is an allegation, not a finding about Meta. It is also why the evidence must reach beyond the final click.

    The performance record is now central

    Meta’s declaration places substantial weight on performance history. The company says ratings came from its standard review process and were assigned by managers without AI-assisted scoring or ranking. For one visa-sponsored plaintiff, Meta says the relevant rating predated the worker’s health condition and leave request. For another, Meta says thirteen people in the same cohort who were on leave or had recently taken leave were retained.

    Those details cut against a simple claim that leave automatically produced selection.

    They do not prove every rating, cohort or decision was free from bias. The workers would need evidence showing where the process broke down: an inaccurate rating, a leave-related gap treated as poor performance, a distorted comparison group, a hidden input, inconsistent application or an outcome pattern unexplained by the stated business rationale.

    That is a more demanding case than saying an AI system made a list. It is also the right evidentiary question.

    What an audit should show

    Whether a workforce decision is automated, assisted or manual, an accountable employer should be able to reconstruct it.

    A credible record should show the business rationale, permitted criteria, source of each input, who applied the rule, what was excluded, how leave and accommodation were protected, what outcome testing was conducted and what happened when a result looked wrong.

    The audit should reach backwards. Testing only the final list can miss problems embedded in performance records or group definitions. Testing only the software can miss decisions made through policy, management pressure or organisational design.

    The opposite error is also possible. Calling a process “AI-driven” merely because AI existed somewhere in the workplace can exaggerate the technology’s role and obscure the actual decision-makers.

    The purpose of an audit is not to find a machine to blame. It is to find the point at which evidence, judgement and responsibility entered the process.

    Private arbitration may limit what the public learns

    The dispute is expected to move largely through individual arbitration because of Meta’s employment agreements. Arbitration may resolve the workers’ claims. It may also mean less evidence, testimony and reasoning becomes public than in open court.

    That matters beyond Meta. Employers are introducing AI into performance management, recruitment, productivity measurement and workforce planning. Courts, regulators, workers and other companies need examples showing what a defensible process looks like and what evidence reveals a discriminatory one.

    The question after “people decided”

    Meta has put forward a detailed account: people made the decisions, criteria were documented, AI was excluded, protected information was unavailable to decision-makers and individual selections had business explanations.

    That account should not be dismissed because it complicates the original allegation. It should be tested.

    The workers’ allegations should be treated the same way. They are serious, but remain allegations until supported by evidence and decided through the appropriate process.

    When a company says a person made the decision, the next question is not whether that person clicked the final button. It is whether the entire decision chain can be reconstructed, examined and defended.

    Principal sources

    Editorial disclosure: This article was developed with assistance from artificial intelligence. Its sources, claims and conclusions were reviewed by Immortal AI’s editor before publication.

  • Your Employer Introduced AI. Did Anyone Tell You What It Would Do to You?

    Your Employer Introduced AI. Did Anyone Tell You What It Would Do to You?

    NEWS & ANALYSIS | WORK & POWER

    Across workplaces in the United States, Europe, Asia and Australia, artificial intelligence is moving beyond assistance. It is helping assign work, monitor performance, evaluate workers and shape decisions about employment. The systems differ. The central question does not: what does their introduction do to the people expected to work with them?

    By Andrew McDonald · Immortal AI

    The new system usually arrives with a demonstration.

    It can summarise meetings, draft reports, rank applications, allocate shifts, monitor performance or automate the routine parts of a job. Management talks about productivity. Workers receive a login, a short training session and perhaps a new target.

    Then the less visible work begins.

    Someone must check the output. Someone handles the exceptions the system cannot resolve. Simple tasks disappear while the difficult ones remain. A worker may be responsible for a decision they cannot explain, measured by software they cannot inspect or expected to achieve a pace calculated from an optimistic trial.

    AI is not only an information-technology purchase when it changes workload, autonomy, surveillance, responsibility or the way performance is judged. It is a change to the conditions in which people work.

    AI is already managing work

    Algorithmic management is no longer confined to warehouses, delivery platforms or experimental workplaces.

    An OECD study published in 2025 surveyed more than 6,000 mid-level managers in France, Germany, Italy, Japan, Spain and the United States. Seventy-four per cent said their firms used at least one software tool to instruct, monitor or evaluate workers. Reported adoption was highest in the United States at 90 per cent, averaged 79 per cent across the European countries surveyed and was 40 per cent in Japan.

    Those figures require care. They are based on managers’ reports, and algorithmic management can include software that does not use artificial intelligence. They do not prove that every system is harmful or that every worker is constantly monitored.

    They do show that automated management is already part of ordinary working life. In the same research, nearly two-thirds of managers reported at least one concern about the trustworthiness of the systems they used. The most common concerns involved unclear accountability, difficulty following the logic of decisions and inadequate protection of workers’ physical or mental health.

    The International Labour Organization provides a wider measure of potential reach. Its 2025 global index estimated that one in four workers is in an occupation with some exposure to generative AI. The ILO’s conclusion was not that one in four jobs would disappear. Transformation of tasks was more likely than complete replacement.

    That distinction moves attention from a distant argument about mass unemployment to a present question: how is the job changing now?

    The work AI leaves behind

    Automation is often described as removing repetitive tasks. That can be beneficial. A system that handles dangerous, tedious or administratively heavy work may reduce exposure to harm and give people more time for valuable tasks.

    But removing simple work does not automatically make a job easier.

    When routine tasks are automated, workers can be left with a greater concentration of work requiring sustained attention, judgement or emotional effort. Monitoring can support safety, such as identifying fatigue, but it can also increase pressure or become a performance-management tool. A system introduced for security can gradually be used to judge speed, tone, attendance or behaviour.

    A flawed customer system may not only frustrate the customer. It can redirect anger toward the worker required to defend or repair it. A decision system may act quickly, but the person held accountable may lack the authority or information needed to challenge it.

    Research reviewed by the OECD and ILO describes both sides of this change. Workers and managers can report better performance, reduced repetitive work and greater job satisfaction. The same evidence identifies work intensification, surveillance-related stress, privacy concerns, reduced interaction and doubts about automated decisions.

    These outcomes are not inevitable. They are foreseeable design and management risks. That matters because a foreseeable risk can be investigated before harm becomes the evidence that something went wrong.

    Different rules, the same underlying responsibility

    Countries are responding through different legal and regulatory systems.

    The European Union’s AI Act treats certain systems used in recruitment, worker selection, promotion, dismissal, task allocation and performance monitoring as high-risk. Its requirements are being phased in, and the exact obligations depend on the system and its use. The significance is broader than Europe: employment decisions are recognised as a setting where AI can materially affect a person’s rights and livelihood.

    Australia offers a different example. In July 2026, Safe Work Australia published guidance stating that artificial intelligence and digital technologies can introduce or increase physical and psychological risks. Its central position is deliberately practical: those risks should be managed through the same workplace health and safety process used for other hazards.

    That does not create one global legal rule. Labour law, privacy protection, consultation duties and enforcement vary widely between jurisdictions. In many places, regulation remains fragmented or incomplete.

    But the management question travels. If a system changes workload, surveillance, autonomy, responsibility or exposure to harm, an employer cannot reasonably treat those effects as irrelevant simply because the product came from an external vendor.

    Consultation is not an announcement

    Worker consultation is one of the clearest points of agreement across the evidence.

    OECD research found that training and worker consultation were associated with better outcomes for workers. Safe Work Australia says risks should be identified, assessed, controlled and reviewed in consultation with workers and their representatives. European rules create more formal duties for some high-risk uses.

    Exact legal obligations depend on the country, employment arrangement and applicable workplace instrument. The practical reason for consultation is more universal.

    The person doing the job knows where workarounds occur, which cases do not fit a standard process and what happens when a system is slow, wrong or unavailable. Excluding that knowledge can make an AI project less safe and less useful.

    Consultation does not mean announcing a finished decision. Workers should be involved early enough to influence the design, not asked for feedback after the targets, vendor and operating model have been fixed.

    Who receives the productivity gain?

    There is credible evidence that workers and employers can benefit from AI. The OECD’s cross-country worker research found generally positive reports about performance and working conditions among workers already using AI. Managers in its later algorithmic-management survey also commonly reported improved decision quality and job satisfaction.

    Those findings should not be dismissed, but they should not be overstated. Survey responses are not independent measurements of every workplace, and positive results in one occupation do not establish what happens in another.

    The distribution question remains.

    If AI saves a workday, is that time returned through lower workload, better service, learning or shorter hours? Or is the expected output increased until the person is busy again?

    A productivity gain can become a safety risk if it is converted automatically into higher demands. It becomes a power question if the financial benefit flows upward while workers absorb retraining, uncertainty, surveillance and intensified work.

    What a responsible introduction looks like

    A responsible employer should be able to explain what the system does, what data it uses and what decision it influences. Workers should know when they are interacting with AI, when their activity is being monitored and whether the resulting information can affect performance management, rostering, promotion or dismissal.

    The assessment should consider more than technical failure. It should ask whether workloads will change, whether complex work will concentrate, whether staffing assumptions depend on unverified productivity claims and whether disabled workers, carers, older workers, culturally diverse teams or people in insecure work will experience the change differently.

    There should be a clear way to challenge an output and a person with authority to correct it. A worker should not be required to accept responsibility for a decision while being denied the power or information needed to review it.

    Training should include the system’s limitations, not only instructions for using it. Time spent checking AI output is work and should appear in workload planning. Vendor updates should trigger consideration of whether the risk profile has changed.

    Controls also need review. A safe pilot does not prove that the same system will remain safe when expanded to a larger team, linked to more data or used for a different purpose.

    What workers can ask

    A worker does not need to be an AI specialist to ask useful questions.

    What task is the system changing? What information about workers does it collect? Who sees that information? Can it be used in performance or employment decisions? How was the expected time saving calculated? Who checks errors? Can a person override the output? What happens when the system fails? Were workers or their representatives consulted? When will the effects be reviewed?

    Those questions do not oppose innovation. They test whether an employer understands the change it is asking people to carry.

    Concerns can be raised through the channels available in the relevant workplace and country, including management, worker representatives, unions, safety bodies, labour regulators, data-protection authorities or professional advisers. Available rights and remedies will differ by jurisdiction.

    The decision before the decision

    AI debates often jump from possibility to outcome. Will it replace jobs? Will it make everyone more productive? Will it transform an industry?

    The first responsibility is closer and more immediate.

    Before the new target is set, before a performance score is trusted and before a role is redesigned, somebody must examine what the system changes for the person doing the work.

    The global evidence does not support one simple verdict on workplace AI. It can remove danger, reduce repetitive work and improve decisions. It can also intensify work, extend surveillance and move responsibility onto people who cannot inspect or correct the system.

    A workplace should not have to wait for exhaustion, unfair treatment or an incident to discover which version it introduced.

    The tool may be new. The obligation to ask people what it will do to them is not.

    Principal sources

    This article was prepared with AI assistance for research organisation and drafting. Sources were checked against the original publications. It provides general information, not legal advice. Editorial responsibility remains with Immortal AI.

  • AI Rejected You. Who Is to Blame?

    FOUNDATION INVESTIGATION | WORK & DECISIONS

    AI can rank, score and filter people without ever making the final decision itself. When the outcome harms you, responsibility should not disappear between the model, the manager and the company.

    By Andrew McDonald · Immortal AI

    You apply for a job.

    You never speak to a person.

    A system scores your application, compares your history with other candidates and decides you are not worth moving forward.

    Or your shifts fall. Your credit application is declined. A government payment is questioned. A risk score appears beside your name.

    You ask why.

    The company says the software only assisted the decision.

    The software provider says it does not control how customers use the system.

    The manager says they relied on the information they were given.

    The machine says nothing.

    AI rejected you. Who is to blame?

    The final click does not tell us who made the decision

    Organisations often reassure people that AI does not make important decisions because a person remains “in the loop”.

    That can be meaningful.

    It can also be cosmetic.

    A manager who independently reviews evidence, understands a system’s weaknesses and has real authority to reject its recommendation is exercising judgement.

    A manager who receives a score, sees little of the underlying logic and is expected to approve the recommended outcome may be little more than the final step in an automated process.

    That distinction is already central to our reporting on Workers Say AI Marked Them for Layoff. Meta Says People Decided. The unresolved question in that case is not merely whether a person technically approved a layoff. It is what information shaped that judgement and whether the decision could genuinely be challenged.

    Algorithmic management is already normal

    This is not a problem waiting for some distant AI workplace.

    The OECD defines algorithmic management as software that fully or partly automates tasks traditionally performed by managers, including instructing, monitoring and evaluating workers.

    Its survey of more than 6,000 firms across six countries found these tools were already common. A later OECD policy brief reported that 90 per cent of surveyed US firms and an average of 79 per cent of surveyed European firms used at least one algorithmic-management tool. OECD: How widespread is algorithmic management in workplaces?.

    Managers saw benefits, including improved decision quality.

    They also identified a problem that goes directly to accountability: the most commonly reported concern was uncertainty about who was responsible when an algorithmic decision went wrong. OECD employer survey on algorithmic management.

    The technology is becoming ordinary before responsibility has become clear.

    A score can be a decision even when somebody else acts on it

    European law provides one useful example of why labels such as “recommendation” or “score” may not settle the issue.

    Article 22 of the GDPR gives people protections against certain decisions based solely on automated processing that produce legal or similarly significant effects. Where specified exceptions apply, safeguards include the ability to obtain human intervention, express a point of view and contest the decision. GDPR Article 22.

    In the SCHUFA case, the Court of Justice of the European Union considered a credit-scoring company that generated a probability value which lenders then used.

    The court held that creating that score can itself amount to automated individual decision-making where the third party relies strongly on it when deciding whether to enter, continue or end a contract. CJEU, SCHUFA, C-634/21.

    That principle matters well beyond credit scoring.

    An automated output does not become harmless simply because somebody else performs the last action.

    The person affected usually knows the least

    There is a severe information imbalance in automated decision-making.

    The company knows which system was used.

    The vendor knows how the product was designed.

    The organisation may know what data were entered, what score was produced and how heavily it was weighted.

    The person affected may receive only the outcome.

    That makes an appeal difficult before it even begins.

    How do you challenge incorrect data you cannot see? How do you identify discrimination when the relevant variables are hidden? How do you argue that a manager relied too heavily on a recommendation when you do not know the recommendation existed?

    As the OECD noted again in July 2026, policy measures specifically addressing AI in labour markets remain less developed in areas such as privacy, transparency and accountability than in skills and adoption. OECD: Recent policy developments on AI in the labour market.

    Australia has already seen what inaccessible automation can do

    The Robodebt Royal Commission was not an investigation into generative AI.

    It remains one of Australia’s clearest warnings about automated government decisions and weak avenues for challenge.

    The Royal Commission recommended that where automated decision-making is used in government services, people should have a clear path to review, websites should explain the process in plain language, and business rules and algorithms should be available for independent expert scrutiny. It also recommended an independent monitoring or audit function for automated decision-making. Robodebt Royal Commission, Chapter 17.

    Those recommendations capture a basic principle.

    An appeal right is not meaningful if the person cannot find the decision-maker, understand the process or discover what needs to be challenged.

    Blame can become fragmented by design

    AI systems are often supplied through chains of responsibility.

    A vendor builds the model.

    A software company packages it into a product.

    An employer chooses the settings.

    A manager receives the output.

    A worker experiences the consequence.

    Each participant can truthfully say they controlled only part of the process.

    That does not mean nobody is responsible.

    It means responsibility needs to follow control.

    The developer should be accountable for the system it designs and the claims it makes about performance. The organisation deploying it should be accountable for deciding whether the system is appropriate, what data it uses, how outputs influence decisions and whether people can challenge them. Managers should be responsible where they exercise genuine judgement.

    The person affected should not be expected to solve the organisational chart before they can appeal.

    A real appeal needs more than another review by the same system

    A meaningful appeal should provide several things.

    The person should know that automation materially influenced the decision.

    They should be able to identify and correct relevant data.

    They should receive an explanation sufficient to understand the main reasons for the outcome.

    And the reviewer should have the authority, information and competence to change the result.

    Sending the same information through the same scoring process again is not independent review.

    Nor is a person automatically an effective safeguard merely because their name appears at the end of the workflow.

    This is why the broader power question matters. In AI’s Promise Is Real. So Is the Power Shift., we argued that organisations can deploy technology far faster than workers and communities can negotiate the consequences. Automated decisions make that imbalance intensely personal.

    Someone must own the outcome

    AI can improve consistency, identify patterns people miss and help organisations make faster decisions.

    Those benefits are real.

    But efficiency cannot become a method for making responsibility harder to find.

    The more an organisation relies on automated ranking, scoring or recommendation, the clearer its obligations should become.

    Who approved the system?

    Who monitors it?

    Who can explain an outcome?

    Who can override it?

    Who is responsible when it harms somebody?

    Our Immortal AI Foundations start with the consequence for people and follow the evidence toward those with the power to shape it. Automated decision-making should be judged by the same standard.

    If an algorithm has ever rejected, ranked or investigated you, ask who was responsible for the decision and whether a real appeal was available.

    Because when a company, provider, employee and machine can all point somewhere else, the person carrying the consequence is left with the only answer that matters:

    someone still made a choice to use the system.

    Principal sources

    Editorial note: Legal rights differ by jurisdiction and context. This article is analysis, not legal advice.

    AI disclosure: Immortal AI uses AI-assisted research and drafting. Sources, claims, framing and final editorial decisions remain the responsibility of Immortal AI.

  • Workers Say AI Marked Them for Layoff. Meta Says People Decided.

    Workers Say AI Marked Them for Layoff. Meta Says People Decided.

    NEWS & ANALYSIS | WORK & POWER

    Twenty-six Meta employees say AI-assisted systems helped put them on a layoff list after they took protected medical, parental or family leave, or received disability accommodation. Meta says the claim is wrong and that people made the decisions. A federal judge has refused to stop the layoffs, but the court has not decided whether the workers’ allegations are true.

    By Andrew McDonald · Immortal AI · Part One

    For years, one of the simplest promises made about artificial intelligence in the workplace has been that a person would remain responsible for important decisions.

    That distinction is now being tested in a case involving 26 Meta employees who say the formal decision may have belonged to people, but the information shaping that decision came from systems they could not see, challenge or properly interrogate.

    The workers filed a lawsuit in California alleging that Meta used a collection of internal AI and algorithmically assisted systems to score, rank and help select employees during a workforce reduction affecting about 8,000 jobs.

    Meta denies it.

    “Workforce management and organizational decisions were and are made by people, not AI,” the company said in response to the claims.

    That leaves a question that matters well beyond Meta.

    When is a person’s decision really human?

    What the workers allege

    The 26 plaintiffs say they had taken protected medical, parental, pregnancy, caregiving or family leave, or had requested or received disability accommodation.

    Their complaint alleges that Meta’s layoff process relied on a “constellation” of systems and signals, including internal AI tools, keystroke and activity-monitoring information, AI-token-usage dashboards and algorithmically assisted performance rankings.

    The allegation is not simply that a machine produced a list and automatically fired people.

    It is that the systems used to evaluate activity and productivity could disadvantage workers whose legitimate absence from work meant they had fewer opportunities to generate the signals being measured.

    According to the complaint, those scores and ratings could not be accumulated in the same way by someone who was on protected leave or whose output was reduced by a disability. The workers say Meta failed to neutralise those absences before the information flowed into the layoff process.

    Those allegations have not been proven.

    Meta says the premise is false

    Meta says the lawsuit lacks merit and is not based on facts.

    Its position is direct: people, rather than AI, made workforce and organisational decisions.

    That denial matters. It would be wrong to report the employees’ description of the system as an established account of what happened inside Meta.

    The public record currently contains competing claims. The employees describe AI-assisted and algorithmic systems feeding a selection process. Meta says AI did not make the decisions.

    The unresolved issue sits between those positions.

    A manager can technically approve a decision while relying heavily on a score, ranking or recommendation produced elsewhere. Whether that amounts to meaningful independent judgement depends on how the system was used, what information the manager saw, what discretion existed and whether the underlying data could be challenged.

    The judge did not decide who was right

    The employees sought emergency court intervention to stop their separations while the underlying claims proceed through private arbitration.

    US District Judge William Orrick refused to issue that temporary restraining order.

    That was not a finding that Meta had disproved the allegations.

    The judge concluded that the workers had not met the legal threshold needed for the emergency relief they were seeking. Reuters reported that he nevertheless said the plaintiffs had raised serious questions concerning the alleged use of AI in the layoff process and left open the possibility of reconsidering temporary relief if stronger evidence emerges.

    This distinction is important because a failed application for an emergency order can easily be misread as a failed case.

    It is not.

    The central factual dispute remains unresolved.

    The evidence problem may be the bigger story

    The case exposes a structural problem for workers challenging algorithmically influenced employment decisions.

    The company generally controls the system.

    It knows which data were collected, how rankings were generated, what weighting was used, what managers saw, whether a recommendation could be overridden and how much influence each tool had on the final outcome.

    The employee sees the result.

    Reuters reported that legal experts see proof as a central difficulty in the Meta case, particularly because much of the underlying evidence is internal and the workers’ disputes are moving toward private arbitration.

    This creates an accountability problem even when a person remains formally responsible for the final decision.

    If an organisation can say “a person decided” without explaining what information shaped that person’s judgement, the human decision-maker can become a shield around an automated process rather than a safeguard against it.

    Protected leave makes the allegation more serious

    The workers’ claim has another layer.

    Time away from work for pregnancy, parental responsibilities, medical treatment or disability can reduce activity measures for obvious reasons. If those measures are later treated as evidence of weaker performance without being adjusted for protected absence, the system can reproduce discrimination without ever being instructed to discriminate.

    That does not establish that Meta’s system did so. It explains why the allegation deserves scrutiny.

    A model does not need a field labelled “pregnancy” or “disability” to create unequal outcomes. A proxy such as logged activity, output volume or tool use may correlate with circumstances the law protects.

    This is one reason accountability cannot stop at asking whether an AI explicitly made the final decision.

    When is a person’s decision really human?

    The phrase “human in the loop” has become a reassuring shorthand in discussions about artificial intelligence.

    But a person clicking approve at the end of a process does not automatically make that process meaningfully human.

    The real questions are harder.

    Did the person understand how the ranking was produced? Could they see the relevant limitations? Were protected absences removed from the calculation? Could the worker challenge incorrect data? Was the manager expected to depart from the recommendation? Did doing so carry a cost?

    If the answers are unknown, saying a person made the decision tells us less than it appears to.

    That is the significance of the Meta case even before the allegations are resolved.

    It forces a distinction between human approval and human judgement.

    What we know, and what we do not

    We know that 26 Meta employees filed the case. We know the complaint alleges that internal AI and algorithmically assisted systems contributed to the layoff-selection process. We know every plaintiff had taken protected leave or sought or received disability accommodation. We know Meta rejects the allegations and says people made the decisions. We know the judge refused the workers’ request for an emergency order stopping the layoffs.

    We do not yet know precisely how Meta’s internal systems were weighted in the final selections, whether the plaintiffs’ account of the technology will be supported by internal records, or whether the alleged process unlawfully disadvantaged people who took protected leave.

    Those questions require evidence that has not yet been fully tested in public.

    That uncertainty is not a reason to dismiss the story.

    It is the story.

    Accountability cannot disappear between the model and the manager

    AI does not need authority to fire someone in order to influence who gets fired.

    A system that scores, ranks, filters or recommends can shape the range of decisions a manager believes are reasonable. The more complex and opaque the process becomes, the easier it is for responsibility to become fragmented.

    The developer can say the model only provided information. The manager can say they relied on the company’s systems. The company can say a person made the final decision.

    The employee is still unemployed.

    That is why the standard cannot simply be whether a person appeared somewhere in the chain.

    It has to be whether someone had enough knowledge, authority and responsibility to recognise a bad outcome and stop it.

    Continue this investigation: AI Rejected You. Who Is to Blame? examines accountability when automated systems influence workplace and institutional decisions.

    Coming next: Part Two

    Part Two will follow when the next material evidence emerges. We will examine what new filings reveal about how Meta’s systems actually worked, what managers were shown, and whether the distinction between an AI-assisted recommendation and a human decision survives closer scrutiny.

    Editorial note: The employees’ claims are allegations and have not been proven. Meta denies that AI made the layoff decisions. The court’s refusal to grant emergency relief did not resolve the merits of the underlying allegations.

    Editorial disclosure: Immortal AI uses AI-assisted research and drafting. Material claims in this article were checked against the complaint as described in independent reporting, Meta’s response and reporting on the court ruling. Final editorial decisions remain the responsibility of Immortal AI.

    Principal sources

  • AI’s Promise Is Real. So Is the Power Shift.

    Investigation | Power & Accountability

    By Andrew McDonald · 10 July 2026 · Analysis

    Artificial intelligence could improve healthcare, productivity and access to knowledge. But its development is exposing a harder question: who controls the technology, who receives its benefits, and who carries its costs?

    A worker is told that AI will make her more productive, but there is no guarantee she will share in the gains. A woman discovers that her face has been placed into synthetic sexual material. An Australian household is promised cheaper, cleaner electricity while billion-dollar data centres prepare to draw more power from the same grid.

    These are different harms, but they reveal the same imbalance. The companies and institutions deploying AI can move quickly. Workers, regulators and communities are left to negotiate the consequences after deployment has begun.

    Control Is Concentrated

    The AI industry is not an open contest between equal participants.

    Stanford University’s 2026 AI Index reports that industry produced more than 90 per cent of notable frontier AI models in 2025. It also found that the United States hosts 5,427 data centres, more than ten times the number in any other country, while one Taiwanese company, TSMC, fabricates almost every leading AI chip.

    This does not mean a single company or country controls AI. It means that critical parts of the system, including advanced chips, cloud services, data centres and model development, are concentrated among a limited number of powerful organisations. The OECD has separately warned that cloud computing markets have high concentration, barriers to entry and difficulties for customers seeking to switch providers.

    That concentration matters because organisations controlling the infrastructure can influence the price, availability and rules of access to increasingly important technology.

    Workers Face a Transition, Not a Guaranteed Windfall

    AI will not simply eliminate every exposed job. In many occupations, it is more likely to change tasks, increase output or assist workers.

    The International Labour Organization estimates that AI may affect nearly 80 million workers across Southeast Asia, although it says large-scale job disruption has not yet occurred.

    In Latin America and the Caribbean, World Bank and ILO modelling found that generative AI could improve productivity in 8 to 12 per cent of jobs. However, up to 17 million of those jobs may be unable to realise the benefit because of inadequate digital infrastructure. The study also estimates that 2 to 5 per cent of regional jobs face potential automation, with women twice as likely as men to be in that category. These are estimates of exposure and potential impact, not confirmed job losses.

    The missing element is a fair bargain. Large and well-funded employers can purchase automation, while workers are often expected to fund their own retraining and accept greater uncertainty. Higher productivity does not automatically produce higher wages, reduced working hours or improved job security.

    Synthetic Content Is Creating Real Victims

    AI-generated content is no longer merely a problem of fake celebrity photographs or misleading advertisements.

    UN Women reports that legal systems and platforms are failing many women subjected to AI-enabled deepfake abuse. Europol has also coordinated an international operation involving authorities from 19 countries that resulted in 25 arrests connected to AI-generated child sexual abuse material.

    From 2 August 2026, European Union transparency rules will require clear labelling in key cases involving deepfakes, interactive AI systems and AI-generated or manipulated text concerning matters of public interest.

    Labelling is necessary, but it is not a complete remedy. A label cannot reliably reverse reputational damage after fabricated material has spread.

    The pressure is also reaching science. On 7 July, Nature reported on an academic “humanizer” designed to remove apparent signs of AI use from research papers and grant proposals. AI can help researchers analyse information and communicate findings, but tools designed to conceal its use undermine disclosure and make already strained review systems harder to trust.

    Australia Will Feel the Physical Cost

    The cloud is physical infrastructure.

    The International Energy Agency reports that global data-centre electricity use increased 17 per cent in 2025, while use by AI-focused centres grew 50 per cent. Its central projection has total data-centre consumption rising from 485 terawatt-hours in 2025 to 950 terawatt-hours in 2030.

    In Australia’s National Electricity Market, average data-centre demand was almost 600 megawatts during the first quarter of 2026. AEMO said 11 proposed centres, representing a possible ultimate load of 5.4 gigawatts, were progressing through transmission connection processes. AEMO cautioned that projects are staged and some seek multiple connection options, but expects its demand forecasts to rise materially.

    AI infrastructure can support investment and productivity. The public should still know who will pay for the required generation, networks and storage, and how reliability and household costs will be protected.

    The Standard Must Be Human Benefit

    AI policy is both a technical and political challenge. Technical safeguards matter, but decisions about wages, infrastructure costs, competition and legal rights are choices made by institutions.

    Workers need credible transition plans. Victims of synthetic abuse need fast legal remedies. Communities need transparent information about data-centre power and water use. Governments need independent expertise and the ability to enforce rules against the companies they regulate.

    The question is not whether AI will become more capable.

    It is whether people will retain enough power to determine what those capabilities are used for.

    Without that, AI may improve productivity while automating inequality at scale.

    Continue the investigation: The Data Centre Community Impact Hub brings together the evidence, US project tracker and practical tools for communities assessing data-centre proposals.

    Related investigation: Meta Needed a Data Centre. BlackRock Found the Money. Who Carries the Risk? examines how the physical and financial burden of AI infrastructure is distributed.

    Editorial note: This article synthesises published reporting, official statements and research. It is analysis, not original field reporting.

    Editorial disclosure: This article was developed with assistance from artificial intelligence. Its sources, claims and conclusions were reviewed and approved by Immortal AI’s editor.