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Short answer: AI systems depend on large numbers of people who label, clean, validate and moderate data. Documented problems include low or uncertain pay, insecure contracts, weak protections and exposure to disturbing material. But the evidence available for this topic does not establish that this work is legally slavery or forced labor. “Slave labor” is a provocative description of exploitation, not a settled finding about every AI system or worker.
AI is not trained by software alone
The International Labour Organization (ILO) says AI development requires people who “tag, classify, clean and validate data used in the training of AI systems,” as well as computer scientists and machine-learning specialists. These workers help turn raw text, images, audio and video into material a model can process. They may also check whether an output is accurate, safe or relevant.
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Some workers are hired through microtask or crowdsourcing platforms; others work for business-process-outsourcing (BPO) companies. The ILO says many are based in the Global South. Exact totals are unavailable, but its current overview estimates the broader data-workforce population in the tens of millions. That is an estimate for data work across the digital economy, not a headcount for people who trained one particular chatbot.
“The development and deployment of AI systems requires a vast array of professional skills such as computer scientists and machine learning experts, but also professionals who tag, classify, clean and validate data used in the training of AI systems, as well as in other areas of the digital economy, including e-commerce and social media platforms.”
#1 Best Overall
International Labour Organization
Data labeling and content moderation are different jobs
Data labeling and validation
Labelers might mark objects in an image, identify the language or topic of a passage, rank chatbot answers, transcribe speech, remove duplicates or verify another worker’s decision. Payment can be hourly, per task or governed by a contractor’s terms. The important question is not simply whether a person touched the data, but whether all working time is paid and whether the contract provides meaningful security and recourse.
Content moderation
Moderators review material that platforms or model developers consider unsafe. Depending on the project, that can include graphic violence, hate speech, child exploitation or other disturbing content. Moderation is therefore not interchangeable with routine labeling: repeated exposure can create psychological risks even when the task is performed from an ordinary office or home.
In its 2023 report on social-media work, the ILO lists isolation, high workloads, exposure to disturbing content, abuse and harassment, and lack of job security among the challenges faced by moderators and creators on digital-labour platforms. The list describes documented risks, not conditions experienced identically by every worker.
What the documented risks look like
| Work area | Evidence-based concern | What a responsible investigation should ask |
|---|---|---|
| Labeling, cleaning and validation | Low earnings, limited social protection and occupational-safety concerns are reported across parts of the data-workforce ecosystem. | Is pay hourly or per piece? Is waiting, training and correction time compensated? What benefits and injury protections apply? |
| Content moderation | Disturbing material, heavy workloads, isolation, harassment and insecure employment are identified by the ILO. | Are exposure limits, rotation, trauma-informed training and confidential professional support available? |
| Platform and contractor management | Workers can face opaque metrics, sudden account or contract changes and weak bargaining power. | Can workers appeal decisions, see how performance is measured and challenge unfair treatment? |
These concerns are summarized in the ILO’s accounts of the invisible workforce behind the automated economy and its 2023 social-media-platform publication.
What the Sama assessment actually shows
Sama is a data-annotation company with operations in Kenya and Uganda. Fairwork’s follow-up assessment gave it a 3/10 score for 2024/2025 under Fairwork’s own AI principles. The report records 22 changes or commitments, while saying that evidence remained insufficient for several thresholds, including living-wage pay, social and employment security, management practices and collective representation. A score of 3/10 is an assessment of Sama against Fairwork’s framework; it is not a universal rating of AI companies or proof that every Sama project has identical conditions.
Fairwork’s current AI principles organize scrutiny around pay, conditions, contracts, management and worker representation. They took effect on November 10, 2025, and call for additional trauma-informed safeguards when work may involve traumatic content. Those principles provide a practical way to compare employers without treating a single case as representative of the entire industry.
Rank #3
Read the 2025 Fairwork assessment of Sama and the Fairwork AI principles for the methodology and thresholds.
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What TIME reported about an OpenAI–Sama project
TIME reported in January 2023 that documents it reviewed showed OpenAI signed three contracts with Sama in late 2021 totaling approximately $200,000. The reported work involved labeling text describing sexual abuse, hate speech and violence so that a system could identify harmful material.
Workers interviewed by TIME described psychological harm and questioned the adequacy and availability of counseling. This is evidence about a particular historical project, contractor and period. It should not be expanded into a claim that every OpenAI model, every current project or every AI company uses the same arrangement. TIME’s reporting also does not establish a single universal wage for data workers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does that make it “slave labor”?
The sources cited here document exploitation risks and serious labor-rights concerns. They do not make a legal finding that the described work is slavery or forced labor.
For the literal legal claim to be supportable in a particular case, reporting would need case-specific evidence about coercion, freedom to leave, threats or penalties, control over working conditions and the protections available to workers. Low pay, precarious contracting or distressing work can be abusive without automatically meeting the legal definition of slavery. Conversely, an absence of a legal ruling does not make poor conditions acceptable.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →That is why “slave labor” works better here as an attributed rhetorical accusation than as a factual label for all AI training. A precise account identifies the company, country, task, contract and time period, then tests the conditions rather than collapsing every form of exploitation into one term.
How to evaluate a claim about “ethical” AI labor
When a company says its model was built responsibly, ask for evidence on each of these points:
- Task: Is the work ordinary labeling, safety review, content moderation or several different jobs?
- Pay: What is the effective hourly rate after unpaid screening, waiting, training and corrections, and how does it compare with local wage standards?
- Employment: Are workers employees or contractors? How long do contracts last, and are benefits and social protections provided?
- Exposure and safety: Are harmful-content limits, rotation, safety procedures and confidential, meaningful mental-health support in place?
- Management: Are performance metrics understandable? Can workers appeal decisions and file grievances without retaliation?
- Worker voice: Can workers organize, bargain collectively or participate in decisions affecting their work?
These questions draw on the labor problems identified by the ILO and the evaluation categories in Fairwork’s principles. They are a reporting and procurement checklist, not a claim that every employer has been assessed on every item.
AI may change jobs without eliminating the human workforce
The labor story is not simply “AI replaces people.” In its global analysis of generative AI and jobs, the ILO concluded that generative systems are more likely to augment many jobs than fully automate them, while stressing the need for policies that protect job quality and support fair transitions. That finding concerns economy-wide employment effects; it is separate from the conditions faced by data annotators and moderators.
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