Socioeconomic and environmental harms

Mitigation strategy

1. Establish and enforce a global regulatory framework to ensure ethical labor standards throughout the AI supply chain, focusing on data annotation and content moderation. This includes mandating fair wage structures, secure employment contracts, comprehensive mental health provisions for exposure to toxic content, and complete supply chain transparency to ensure accountability among AI developers and outsourcing firms. 2. Implement targeted policy interventions and market incentives—such as R\&D tax credits and specific regulatory guidance—to strategically redirect AI development towards applications that complement, augment, and upskill human labor across all sectors, rather than solely focusing on cost-cutting automation, thereby mitigating the exacerbation of wage and skill-based inequality. 3. Institute mandatory, standardized protocols for the measurement, disclosure, and auditing of the environmental footprint (energy consumption, carbon, and water usage) associated with AI training, deployment, and inference. This must be complemented by regulatory efforts that promote the adoption of highly efficient AI models (e.g., SLMs) and the transition to renewable energy sources for data center infrastructure.