Biased statements and recommendations

Mitigation strategy

1. Prioritize Data Curation and Preprocessing Implement rigorous preprocessing techniques to ensure the training and fine-tuning data are highly representative and diverse, utilizing methods such as reweighting and resampling to mitigate inherent historical and sampling biases at the source. 2. Establish Continuous Auditing Deploy a comprehensive post-hoc monitoring framework that employs established fairness metrics (e.g., statistical parity, equalized odds) and continuous adversarial testing to proactively detect and quantify emergent bias drift in deployed model outputs across salient protected characteristics. 3. Integrate Transparency and Oversight Mandate the integration of Explainable AI (XAI) tools to elucidate model decision paths, coupled with a robust human-in-the-loop (HITL) protocol to review and, where necessary, override high-stakes recommendations to prevent biased decision-making.