From Incidents to Insights - Patterns of Responsibility Following AI Harm

Explore patterns of responsibility and accountability in the aftermath of AI-related harms through this 15-minute conference talk examining how incidents translate into actionable insights. Analyze case studies and frameworks that reveal how responsibility is distributed among stakeholders when AI systems cause damage or negative outcomes. Investigate the complex web of accountability involving developers, deployers, regulators, and users in AI harm scenarios. Learn about emerging patterns in how organizations, institutions, and individuals respond to AI incidents and the lessons that can be extracted from these responses. Examine the societal and policy implications of different approaches to assigning responsibility for AI harms, including legal, ethical, and practical considerations. Discover how incident analysis can inform better practices for preventing future AI-related damages and establishing clearer accountability structures in AI development and deployment.