If Robots Cause Harm, Who is to Blame? Self-Driving Cars and Criminal Liability
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Criminal Liability
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Abstract This chapter builds on the conceptual groundwork in Chapter 1. Specifically, it confronts my definitions of hypocritical blame and (dismissing) standingless blame with four complications, the first three of which represent forms of nonparadigmatic blame: (1) private blame, (2) self-blame, (3) third-party blame, and (4) degrees of (standing to) blame. The chapter also examines and rebuts several arguments against the notion of standing to blame and defends a commitment-focused account of what undermines standing to blame.
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Intuitively, we lack the standing to blame others in light of moral norms that we ourselves don't take seriously: if Adam is unrepentantly aggressive, say, he lacks the standing to blame Celia for her aggressiveness. But why does blame have this feature? Existing proposals try to explain this by reference to specific principles of normative ethics – e.g. to rule-consequentialist considerations, to the wrongness of hypocritical blame, or principles of rights-forfeiture based on this wrongness. In this paper, I suggest a fundamentally different approach. Employing Timothy Williamson's idea of 'constitutive rules' of speech acts, I argue that this feature of blame is simply constitutive of any essentially moral form of disapproval. So if Adam had the standing to disapprove of Celia's aggressiveness in some form, necessarily, this disapproval couldn't be blame. If I'm right, this proposal thus not only answers our main question, but also sheds an interesting novel light on the very nature of blame. If we didn't have a form of disapproval with that feature, we wouldn't have our practice of holding each other to moral norms.
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An important challenge in the field of law is the attribution of responsibility and blame to individuals and organisations for a given harm. Attributing legal responsibility often involves (but is not limited to) assessing to what extent certain parties have caused harm, or could have prevented harm from occurring. This paper presents a causal framework for performing such assessments that is particularly suitable for the analysis of complex legal cases, where the actions of many parties have had a direct or indirect effect on the harm that did occur. This framework is evaluated by means of a case study that applies it to the Baby P. case, a high-profile case of child abuse leading to the death of a child that has been the subject of a number of public inquiries in the UK. The paper concludes with a discussion of the framework, including a roadmap of future work and barriers to adoption.
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The slavery debate, especially as it concerns the demand for reparation, was recently re-ignited by Henry Gates Jnr in an Op-ed to the New York Times on April 22 2010 ('Ending the Slavery Blame Game') where he contended that the role played by Africans is often underplayed in the slavery blame game.
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Blame typically follows an adverse event, blame of self and blame of others. However, blame confounds efforts to identify and fix fatal systems flaws. Outlined here are four practical techniques for overcoming blame and identifying systems. Most importantly, blame must be expected and recognized in its many aspects; once recognized, it can be overcome. Other' approaches include isolating decisions about discipline from the systems review process, distinguishing between an adverse clinical outcome and a systems flaw, and asking the right questions atthe beginning of analysis.
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Healthcare systems need to consider not only how to prevent error, but how to respond to errors when they occur. In the United Kingdom's National Health Service, one strand of this latter response is the 'No Blame Culture', which draws attention from individuals and towards systems in the process of understanding an error. Defences of the No Blame Culture typically fail to distinguish between blaming someone and holding them responsible. This article argues for a 'responsibility culture', where healthcare professionals are held responsible in cases of foreseeable and avoidable errors. We demonstrate how healthcare professionals can justifiably be held responsible for their errors even though they work in challenging circumstances. We then review the idea of 'responsibility without blame', applying this to cases of error in healthcare. Sensitive to the undesirable effects of blaming healthcare professionals and to the moral significance of holding individuals accountable, we argue that a responsibility culture has significant advantages over a No Blame Culture due to its capacity to enhance patient safety and support medical professionals in learning from their mistakes, while also recognising and validating the legitimate sense of responsibility that many medical professionals feel following avoidable error, and motivating medical professionals to report errors.
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Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people's reactive attitudes towards machines, designers, and users. The results of three studies (N = 1,153) indicate differences in how blame is attributed to these actors. Whether AI systems were explainable did not impact blame directed at them, their developers, and their users. Considerations about fairness and harmfulness increased blame towards designers and users but had little to no effect on judgments of AI systems. Instead, what determined people's reactive attitudes towards machines was whether people thought blaming them would be a suitable response to algorithmic harm. We discuss implications, such as how future decisions about including AI systems in the social and moral spheres will shape laypeople's reactions to AI-caused harm.
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