Design of AI

Mitigation strategy

1. Implement rigorous, continuous auditing of AI systems and their training datasets for bias, utilizing diverse and representative data collection practices and employing algorithmic fairness techniques to prevent the perpetuation of societal inequities. 2. Establish a human-centered design framework and governance model that integrates diverse, cross-functional teams (including ethicists and affected community representatives) throughout the AI lifecycle to ensure that system goals prioritize equitable societal benefits over power centralization. 3. Mandate the use of Explainable AI (XAI) techniques and clear documentation to ensure transparency in AI decision-making processes, thereby enhancing accountability and allowing stakeholders to verify that outcomes are based on ethically sound principles.