Responsible AI

Responsible AI examines how AI systems affect people and society, and how to prevent or address harms while preserving useful applications. Approaches include testing for bias, robustness, privacy, and explainability; documenting data and models; conducting impact assessments; and designing for human oversight and community participation. The field also studies laws, organizational practices, incident reporting, and environmental costs across the AI lifecycle, from development through deployment. A central challenge is turning broad ethical principles into effective, context-sensitive practices and enforceable accountability while balancing competing goals such as accuracy, fairness, autonomy, and efficiency.