Discrimination, toxicity, and bias

Mitigation strategy

1. Implement rigorous, continuous bias audits and data governance, spanning from training data selection (to ensure diversity and representation) through post-deployment monitoring, actively remediating systemic bias encoded in proxy variables and flawed feature selection. 2. Establish mandatory human-in-the-loop oversight and clear accountability frameworks, particularly for high-risk applications such as employment and service access, enabling human review and override of potentially discriminatory algorithmic decisions. 3. Advance algorithmic transparency and explainability by adopting interpretable models and deploying Explainable AI (XAI) techniques, thereby allowing for the systematic analysis and articulation of a model's decision-making process concerning equitable outcomes.