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6. Socioeconomic and Environmental3 - Other

Societal inequality

Societal inequality - Increased difference in social status or wealth between individuals or groups caused or amplified by a technology system, leading to the loss of social and community wellbeing/cohesion and destabilisation.

Source: MIT AI Risk Repositorymit976

ENTITY

2 - AI

INTENT

3 - Other

TIMING

3 - Other

Risk ID

mit976

Domain lineage

6. Socioeconomic and Environmental

262 mapped risks

6.2 > Increased inequality and decline in employment quality

Mitigation strategy

1. Prioritize the collection and use of Diverse, Representative Data and Bias-Aware Algorithms. Developers must actively seek datasets that accurately reflect the target population's diversity, and employ bias-aware computational models and techniques (preprocessing, in-processing, post-processing) to explicitly identify and mitigate algorithmic biases concerning protected attributes such as race, gender, and age. 2. Mandate Algorithmic Auditing and Promote Explainability (XAI). Establish standardized frameworks for rigorous ethical audits, stress tests, and regular bias impact assessments of AI systems to ensure equitable decision-making and outcomes. Require Explainable AI (XAI) in high-risk applications to ensure transparency in how decisions are reached. 3. Invest in Digital Infrastructure, Education, and Workforce Reskilling. Governments and organizations must prioritize universal access to robust and affordable digital infrastructure to bridge the 'digital divide'. Simultaneously, significant investment is needed in STEM education, vocational training, and workforce reskilling programs, coupled with robust social safety nets, to ensure equitable opportunity and mitigate job displacement.