This is the second of two companion papers that discuss accidents caused by robots. In the first paper (Guerra et al., 2021), we presented the novel problems posed by robot accidents, and assessed the related legal approaches and institutional opportunities. In this paper, we build on the previous analysis to consider a novel liability regime, which we refer to as ‘manufacturer residual liability’ rule. This makes operators and victims liable for accidents due to their negligence – hence, incentivizing them to act diligently; and makes manufacturers residually liable for non-negligent accidents – hence, incentivizing them to make optimal investments in R&D for robots' safety. In turn, this rule will bring down the price of safer robots, driving unsafe technology out of the market. Thanks to the percolation effect of residual liability, operators will also be incentivized to adopt optimal activity levels in robots' usage.

In robot torts, robots carry out activities that are partially controlled by a human operator. Several legal and economic scholars across the world have argued for the need to rethink legal remedies as we apply them to robot torts. Yet, to date, there exists no general formulation of liability in case of robot accidents, and the proposed solutions differ across jurisdictions. We proceed in our research with a set of two companion papers. In this paper, we present the novel problems posed by robot accidents, and assess the legal challenges and institutional prospects that policymakers face in the regulation of robot torts. In the companion paper, we build on the present analysis and use an economic model to propose a new liability regime which blends negligence-based rules and strict manufacturer liability rules to create optimal incentives for robot torts.

In this chapter, we build on the existing literature on the use of legal strategies for addressing problems of biased judgment and behavior, exploring how heuristics and biases may be exploited to foster efficiency in the presence of other incentive alignment problems. We also introduce two new categories: the hitherto unnoticed counterparts to debiasing and insulating strategies, which we will call “benevolent biasing,” and “cognitive leveraging” strategies.

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In robot torts, robots carry out activities that are partially controlled by a human operator. Several legal and economic scholars across the world have argued for the need to rethink legal remedies as we apply them to robot torts. Yet, to date, there exists no general formulation of liability in case of robot accidents, and the proposed solutions differ across jurisdictions. We proceed in our research with a set of two companion papers. In this paper, we present the novel problems posed by robot accidents, and assess the legal challenges and institutional prospects that policymakers face in the regulation of robot torts. In the companion paper, we build on the present analysis and use an economic model to propose a new liability regime which blends negligence-based rules and strict manufacturer liability rules to create optimal incentives for robot torts.