Increasing inequality and negative effects on job quality
Advances in LMs, and the language technologies based on them, could lead to the automation of tasks that are currently done by paid human workers, such as responding to customer-service queries, translating documents or writing computer code, with negative effects on employment.
ENTITY
1 - Human
INTENT
3 - Other
TIMING
2 - Post-deployment
Risk ID
mit255
Domain lineage
6. Socioeconomic and Environmental
6.2 > Increased inequality and decline in employment quality
Mitigation strategy
1. Prioritize strategic, continuous investment in comprehensive reskilling and upskilling programs to cultivate human-AI complementarity skills (e.g., critical thinking, ethical reasoning) and technical competencies, thereby securing new roles that leverage, rather than are replaced by, Large Language Models (LLMs). 2. Establish and significantly reinforce robust social insurance and support mechanisms, including modernizing unemployment insurance and developing dedicated worker transition funds, to mitigate the economic and social costs of LLM-driven labor displacement and reduce disparities in workers' capacity to weather job transitions. 3. Promulgate and enforce governance standards that mandate employer transparency regarding the integration of LLMs in employment processes, coupled with mechanisms to strengthen collective worker representation to ensure equitable job redesign, fair compensation policies, and the responsible management of automation-induced organizational change.
ADDITIONAL EVIDENCE
Unemployment and wages If LM-based applications displace employees from their roles, this could poten- tially lead to an increase in unemployment (Acemoglu and Restrepo, 2018; Webb, 2019), and other longer-term effects.