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6. Socioeconomic and Environmental1 - Pre-deployment

Labor exploitation

Use/misuse of labour to help train, develop, manage or optimise a technology system or set of systems, including under-paid and/or offshore

Source: MIT AI Risk Repositorymit1352

ENTITY

1 - Human

INTENT

1 - Intentional

TIMING

1 - Pre-deployment

Risk ID

mit1352

Domain lineage

6. Socioeconomic and Environmental

262 mapped risks

6.2 > Increased inequality and decline in employment quality

Mitigation strategy

1. Establish a rigorous, auditable supply chain governance framework to ensure contractual compliance with international and national labor laws (e.g., fair wages, maximum working hours, anti-discrimination). This framework must incorporate continuous monitoring and independent ethical audits for all data enrichment and content moderation services to verify adherence to a quality-of-employment standard that extends beyond minimum local requirements. 2. Mandate the implementation of responsible sourcing policies that guarantee a regionally appropriate living wage and comprehensive occupational health and safety protocols for all human-in-the-loop workers. This includes providing immediate and adequate mental health support and establishing stringent exposure limits to graphic, violent, or toxic content to mitigate documented psychological harm. 3. Codify worker empowerment by ensuring all personnel involved in the AI development, training, and optimization process are afforded genuine input into system design, are provided transparent notice regarding the use and collection of their data, and possess non-retaliatory mechanisms to dispute or correct data used in employment-related decisions.