Risk area 6: Environmental and Socioeconomic harms
LMs create some risks that recur with different types of AI and other advanced technologies making these risks ever more pressing. Environmental concerns arise from the large amount of energy required to train and operate large-scale models. Risks of LMs furthering social inequities emerge from the uneven distribution of risk and benefits of automation, loss of high-quality and safe employment, and environmental harm. Many of these risks are more indirect than the harms analysed in previous sections and will depend on various commercial, economic and social factors, making the specific impact of LMs difficult to disentangle and forecast. As a result, the level of evidence on these risks is mixed.
ENTITY
1 - Human
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit226
Domain lineage
6. Socioeconomic and Environmental
6.0 > Socioeconomic & Environmental
Mitigation strategy
1. Energy Efficiency Paradigm Shift Mandate a pivot from monolithic, large-scale general-purpose models toward resource-efficient alternatives, such as smaller, task-specific models and the Mixture of Experts (MoE) architecture, coupled with aggressive inference efficiency optimizations (e.g., model compression and concise prompting). This directly addresses the substantial energy footprint of LLM operation and training. 2. Equitable AI Governance and Auditing Establish robust governance frameworks that require systematic auditing for bias and unequal performance across demographic groups. Mitigation must include employing diverse and representative training data, implementing algorithmic fairness techniques, and ensuring all decisions affecting vulnerable populations are subject to explainable AI (XAI) standards and meaningful human oversight to prevent the amplification of existing social inequities. 3. Proactive Workforce Transition Planning Develop and fund large-scale reskilling and education programs to prepare workers for labor market transformations resulting from automation. This must be coupled with rigorous risk and legal audits of automated workforce decisions to preemptively mitigate risks of job displacement, discrimination claims, and the loss of high-quality employment.