Ethical AI Risks

Mitigation strategy

1. Prioritize Data Integrity and Representativeness: Rigorously examine, preprocess, and sanitize all training and testing datasets to ensure they are diverse and representative across relevant demographic and social groups, thereby mitigating selection, measurement, and prejudice biases inherent in historical data. This foundational step is critical to prevent AI systems from replicating and perpetuating existing societal discrimination. 2. Implement Algorithmic Fairness and Continuous Testing: Employ mathematical techniques, referred to as "algorithmic fairness," to measure potential discriminatory effects across different population groups. Further, mandate extensive pre-deployment testing—including adversarial testing and evaluation for inappropriate feedback loops—to ensure the model’s robustness and reliability in achieving fair outcomes before it is operationalized. 3. Establish a Robust AI Governance and Human Oversight Framework: Institute a formal governance structure that clearly articulates ethical principles (Fairness, Transparency, Accountability) and integrates human monitoring and judgment at appropriate stages of the AI lifecycle. This ensures clear lines of responsibility, provides mechanisms for auditing AI-driven decisions, and maintains the capacity for human intervention to override automated decisions when ethical or contextual concerns arise.