Unfairness and Discrimination

Mitigation strategy

1. Implement Continuous Post-Deployment AI Governance and Auditing: Establish and mandate an AI governance framework with clear accountability for fairness, focusing on continuous post-deployment monitoring of system outputs using established fairness metrics (e.g., disparate impact ratio, equalized odds) to detect and remediate emergent bias in real-world settings. 2. Systematic Data and Algorithmic Bias Remediation: Conduct rigorous, multi-modal auditing of training datasets to identify and mitigate historical and social biases embedded in the source data, followed by the application of algorithmic fairness constraints and fine-tuning techniques to minimize bias propagation during model development and updates. 3. Institutionalize Procedural Fairness and Cognitive Bias Interventions: Institute a policy requiring standardized decision-making processes and mandatory cognitive bias training for personnel involved in model operation or decisions influenced by the AI output, emphasizing techniques such as 'slowing down' and perspective-taking to interrupt automated stereotypic processing.