Identifying AI Hazards and Responsibility Gaps

With the expansion of artificial intelligence (AI) in general, but especially in self-driving cars in the United States, there is an increasing need for new hazard analysis approaches to determine if and how AI contributes to accidents. The original Swiss Cheese model widely used for hazard analyses focuses uniquely on human activities that lead to accidents but cannot address accidents where AI is a possible causal factor. To this end, the Taxonomy for AI Hazard Analysis (TAIHA) is proposed that introduces layers focusing on the oversight, design, maintenance and testing of AI. Three case studies situated in the self-driving domain demonstrate how TAIHA can be used to better understand how AI contributes to accidents. Resulting TAIHA analyses suggest responsibility gaps are emerging in the self-driving car community in the US where humans, either unknowingly or on purpose, sidestep accountability, leading to serious consequences. Recommendations for mitigating these gaps include more regulatory involvement, as well as the need for company-appointed senior lead test engineers, who should become the new first-line actors in any autonomous system operation.