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AbstractThis paper studies the efficient definition of product defects for AI systems with autonomous capabilities. It argues that defining defects in product liability law is central to distributing responsibility between producers and users. The paper proposes aligning the standard for defect with the relative control over and awareness of product risk possessed by the producer and the user. AI systems disrupt the traditional balance of control and risk awareness between users and producers. The paper provides suggestions for defining AI product defects in a way that promotes an efficient allocation of liability in AI-related accidents. It assesses whether the recent EU policy proposal on product liability aligns with this approach.

Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through the input data, the testing of the algorithm and the decision model. Any transparency requirement for algorithms should result in explanations of these biases that are both understandable for the prospective recipients, and technically feasible for producers. Before asking how much transparency the law should require from algorithms, we should therefore consider if the explanation that programmers could offer is useful in specific legal contexts.