Bias and discrimination

Mitigation strategy

1. Collect Diverse and Representative Data: Rigorously audit and curate training datasets to ensure they are fully representative of the target user populations, proactively mitigating selection and representation biases by utilizing pre-processing techniques such as reweighting, resampling, or synthetic data generation for underrepresented subgroups. 2. Implement Robust Governance and Continuous Monitoring: Establish formal AI governance frameworks that mandate regular, independent audits and continuous monitoring of deployed EAI systems for bias drift, utilizing established fairness metrics and real-world user feedback loops to identify and correct discriminatory outcomes throughout the model's lifecycle. 3. Employ Fairness-Aware Algorithms and Human-in-the-Loop Systems: Integrate fairness-aware machine learning techniques, such as adversarial debiasing or fair representation learning, during model training, and enforce a "human-in-the-loop" protocol for high-stakes decisions to ensure human oversight, accountability, and the capacity to override potentially biased AI-generated judgments.