Exploitation in AI development
Outsourcing tasks like data labeling to low-income countries can perpetuate inequality.
ENTITY
1 - Human
INTENT
3 - Other
TIMING
1 - Pre-deployment
Risk ID
mit1068
Domain lineage
6. Socioeconomic and Environmental
6.2 > Increased inequality and decline in employment quality
Mitigation strategy
1. Mandate and independently audit adherence to ethical labor standards and fair wages for all outsourced data workers in the AI supply chain to directly mitigate exploitation and align with federal labor standards. 2. Institute clear, non-retaliatory reporting and input mechanisms, enabling data workers to raise concerns about working conditions and AI systems, thereby centering worker empowerment and organizational transparency. 3. Conduct regular, third-party impact assessments and supply-chain audits to continuously monitor for, and rectify, risks associated with workers' safety, job quality, and the perpetuation of global socioeconomic inequality.