Impact on affected communities
It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the relevant context for the model and to engender trust within these communities.
ENTITY
1 - Human
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit1331
Domain lineage
1. Discrimination & Toxicity
1.3 > Unequal performance across groups
Mitigation strategy
1. Mandate continuous and transparent engagement with affected communities and relevant stakeholders throughout the model lifecycle to proactively solicit and incorporate their unique perspectives and contextual concerns, thereby addressing community participation risk and building trust. 2. Implement rigorous and ongoing impact risk assessments, specifically utilizing disaggregated data analysis and fairness metrics to identify and quantify unequal performance and inequity risk across different demographic groups and affected communities. 3. Establish a formal AI governance framework with clear accountability structures that legally or procedurally mandate the integration of community feedback, ethical oversight, and independent review prior to and following model deployment.