Artificial intelligence, inattention and liability rules

We characterize the socially optimal liability sharing rule in a situation where a manufac- turer develops an artificial intelligence (AI) system that is then used by a human operator (or user). First, the manufacturer invests to increase the autonomy of the AI (i.e., the set of situations that the AI can handle without human intervention) and sets a selling price. The user then decides whether or not to buy the AI. Since the autonomy of the AI remains limited, the human operator must sometimes intervene even when the AI is in use. Our main assumption is that users are subject to behavioral inattention. Behavioral inattention reduces the effectiveness of user intervention and increases the expected harm. Only some users are aware of their own attentional limits. Under the assumption that AI outperforms users, we show that policymakers may face a trade-off when choosing how to allocate lia- bility between the manufacturer and the user. Indeed, the manufacturer may underinvest in the autonomy of the AI. If this is the case, the policymaker can incentivize the latter to invest more by increasing his share of liability. On the other hand, increasing the liabil- ity of the manufacturer may come at the cost of slowing down the diffusion of AI technology.