Social Cohesion and Equity Disruption:
Systemic deployment of biased AI systems could exacerbate existing social discrimination and prejudice at unprecedented scales, while unequal access to advanced AI capabilities may widen socioeconomic disparities and create new forms of social stratification that challenge traditional social order.
ENTITY
1 - Human
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit1459
Domain lineage
6. Socioeconomic and Environmental
6.2 > Increased inequality and decline in employment quality
Mitigation strategy
1. Prioritize Bias Mitigation from the Conception Phase: Implement pre-processing and training-phase technical strategies—such as curating diverse and representative datasets, applying fairness-aware machine learning frameworks (e.g., adversarial debiasing or regularizers), and defining precise, non-biased target outcomes—to prevent the ingestion and perpetuation of historical and systemic biases. 2. Mandate Transparency and Explainable AI (XAI): Integrate robust XAI techniques (e.g., SHAP, LIME) and clear documentation (e.g., Model Cards) to ensure model interpretability, allowing for rigorous fairness audits, identification of disparate impact, and establishing clear accountability for automated decisions, particularly in high-stakes domains. 3. Establish Continuous Ethical Governance and Diverse Stakeholder Oversight: Institute ongoing bias detection, post-deployment monitoring, and algorithmic impact assessments. Furthermore, ensure the active involvement of diverse development teams, social scientists, and impacted community representatives across the entire AI lifecycle to broaden awareness of potential systemic bias and ensure continuous alignment with social equity principles.