Category: Work & Power

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

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.
July 29, 2026
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
July 28, 2026
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.
July 28, 2026