Risk
Low-paid data labelling, reinforcement learning (from human feedback), and content-moderation tasks—the "ghost work" that trains AI systems—disproportionately falls on under-employed persons with disabilities in low- and middle-income countries (LMICs), often under conditions that exacerbate disability. This pattern is documented in academia in decolonial AI literature as a defining feature of algorithmic coloniality.
Mitigation
Living-wage floors should be required for AI data work, and accessibility audits should apply to gig platforms themselves, as opposed to being applied only to the AI products built on top of them. Recognition of labelling labour in AI supply-chain reporting, a right to disconnect, and medical and ergonomic protections should be established.
Illustrative Examples
Education
Education gaps leading toward precarious AI work
A student with a disability may leave the education system early due to lack of accessible supports, barriers in learning environments, or unmet accommodation needs. Without formal credentials or stable pathways into employment, they may be more likely to seek flexible online work to earn income. AI-related gig work such as data labelling or content moderation can appear accessible because it is remote and does not require formal qualifications. As a result, individuals may become more vulnerable to exploitative conditions that can worsen their health or limit opportunities to move into more secure employment.
Employment
Low-paid AI work without protections or supports
People with disabilities may be more likely to take on flexible work such as data labeling or content review to earn income. While this work may provide short-term income, it is often low paid and may lack work-place protections or supports. Because AI gig work is often not classified as formal employment, workers may not have access to workplace accommodations, ergonomic supports, or health benefits. This may lead to untreated injuries or conditions, increasing long-term healthcare needs. In some cases, the tasks require review of graphic or sensitive material and may worsen existing physical or mental health conditions.
Healthcare
Hidden health impacts of AI gig work
Healthcare systems may not recognize AI gig work as a source of occupational strain. Healthcare providers may not always ask about or record AI gig work when discussing a patient’s daily activities or employment. As a result, the demands of this work, such as long hours of screen use, repetitive clicking or typing, irregular schedules, or exposure to distressing content, may not be recognized as part of the person’s health context. This can delay recovery, worsen existing conditions, impact eligibility for care, or increase reliance on healthcare services without reducing the source of strain.
Services
Gig income disrupting access to supports
Services. Public service programs may not account for newer forms of digital gig work when setting eligibility rules. AI gig work is often irregular, with income changing from week to week. Benefits systems may interpret short-term increases in earnings as a sign that the person is no longer eligible for funded supports. This can lead to sudden loss or reduction of services, even when the income is not reliable enough to replace those supports. This can create gaps where people earning income through AI-related tasks do not clearly fit into existing categories. As a result, they may face uncertainty about eligibility or fall through the cracks entirely.