Against the odds: The case for a modal understanding of due care

Duncan Pritchard, Jeffrey Helmreich

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract / Description of output

We examine the question of how to determine negligence. In particular, how do we determine whether those who have caused injury failed to exhibit ‘reasonable care’ toward those they injured? The standard answer in contemporary legal doctrine involves probability—viz., whether the probability that some injurious activity would harm anyone was low enough, in light of both the severity of the harm risked and the burdens of preventing it. We argue that while this probabilistic standard works well enough for the kind of canonical, single-episode cases that have shaped negligence doctrine over the centuries, it leads to problematic consequences when it comes to large-scale and long-term enterprises—‘aggregative agents', as we call them—where even the lowest probability of injury is sure to materialize, and the injurer can reliably foresee it. In particular, in such cases, the low probability of injury does not in itself show that the agent took due care not to inflict it; indeed, it is consistent with the low probability that the agent intended the harm or was entirely indifferent to it. This raises the question of how we might epistemically capture the due care that aggregative agents are meant to take to avoid inflicting harm. We argue that the solution lies not in adapting the classical legal standard for negligence, but rather in accepting that the best understanding of due care, at least as it extends to such large-scale cases, is to be understood along modal rather than probabilistic lines.
Original languageEnglish
Title of host publicationThe Social Epistemology of Legal Trials
EditorsZachary Hoskins, Jon Robson
Place of PublicationNew York
Number of pages16
ISBN (Electronic)9780429283123
ISBN (Print)9780367245535
Publication statusPublished - 17 Feb 2021

Publication series

NameRoutledge Studies in Epistemology


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