This book introduces a novel puzzle, the 'Lorry Driver Paradox', to advance our understanding of moral responsibility beyond current paradigms, to connect moral philosophy and legal scholarship in new ways, and to break new ground in the ethics of AI.
Part 1 introduces the Lorry Driver Paradox as a set of three individually plausible but jointly inconsistent claims. It then develops and defends the concept of strict moral answerability as the most effective solution, making it the central idea of the book, alongside an account of how 'taking responsibility' could amount to a new, hitherto neglected normative power and an exploration of the significance of apologies in our social practices.
Part 2 extends this discussion to the context of artificial intelligence, proposing a major shift in how we currently think about responsibility and AI. It challenges the conventional notion of AI-generated 'responsibility gaps' and, instead, proposes the idea of 'responsibility abundance'. This reframing, it is argued, offers distinct theoretical, dialectical, and practical advantages.
Significant parts of these arguments draw on legal scholarship, particularly considerations about reverse burdens of proof in criminal law and the waiving of state immunity in public international law. On these grounds, the book also pursues the methodological idea of a 'legal lead', that is, the idea that we can advance our understanding of moral responsibility by investigating (selected aspects of) legal responsibility, and not just the other way around.
Part 1 introduces the Lorry Driver Paradox as a set of three individually plausible but jointly inconsistent claims. It then develops and defends the concept of strict moral answerability as the most effective solution, making it the central idea of the book, alongside an account of how 'taking responsibility' could amount to a new, hitherto neglected normative power and an exploration of the significance of apologies in our social practices.
Part 2 extends this discussion to the context of artificial intelligence, proposing a major shift in how we currently think about responsibility and AI. It challenges the conventional notion of AI-generated 'responsibility gaps' and, instead, proposes the idea of 'responsibility abundance'. This reframing, it is argued, offers distinct theoretical, dialectical, and practical advantages.
Significant parts of these arguments draw on legal scholarship, particularly considerations about reverse burdens of proof in criminal law and the waiving of state immunity in public international law. On these grounds, the book also pursues the methodological idea of a 'legal lead', that is, the idea that we can advance our understanding of moral responsibility by investigating (selected aspects of) legal responsibility, and not just the other way around.
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