AI-based automation increases income inequality
It seems quite plausible that progress in reinforcement learning and language models specifically could make it possible to automate a large amount of manual labour and knowledge work respectively [35, 45, 69], leading to widespread unemployment, and the wages for many remaining jobs being driven down by increased supply.
ENTITY
1 - Human
INTENT
1 - Intentional
TIMING
2 - Post-deployment
Risk ID
mit897
Domain lineage
6. Socioeconomic and Environmental
6.2 > Increased inequality and decline in employment quality
Mitigation strategy
1. Prioritized Investment in Workforce Transformation: Establish comprehensive, publicly and privately financed upskilling and reskilling programs (e.g., tax-advantaged worker accounts or employer tax credits) to rapidly transition the workforce toward high-demand roles that require skills complementary to AI, such as ethical reasoning, critical thinking, and human-in-the-loop coordination. 2. Expansion and Modernization of Social Security Mechanisms: Develop robust and portable social safety nets, including mechanisms for financially supporting displaced individuals (e.g., expanded unemployment benefits or exploration of Universal Basic Income models) and reforming benefit structures (e.g., portable health benefits, reduced retirement vesting) to accommodate increased labor market fluidity and "job churn". 3. Incentivizing Augmentation-Focused AI Deployment and Profit Sharing: Implement policies that structurally incentivize organizations to adopt AI for workforce augmentation and productivity enhancement rather than wholesale job displacement. This should include regulatory support for novel work models, such as tax credits for job creation in emerging industries or experimentation with reduced working hours (e.g., a four-day workweek) to distribute productivity gains broadly across the workforce.