Inequality

Mitigation strategy

- Implement stringent data governance protocols and rigorous data auditing to ensure training datasets are diverse, representative, and balanced across sensitive demographic attributes via pre-processing techniques like reweighting or synthetic data generation. - Apply fairness-aware algorithmic constraints and in-processing techniques (e.g., adversarial debiasing or fair representation learning) during model training to minimize algorithmic bias and promote equitable decision outcomes. - Establish a continuous and robust AI governance framework that mandates post-deployment performance monitoring for bias drift, incorporates Explainable AI (XAI) for transparency, and defines clear human-in-the-loop processes for critical decision oversight.