The use of evidence to resolve uncertainties is key to many endeavours, most conspicuously science and law. Despite this, the logic of uncertainty is seldom taught explicitly, and often seems misunderstood. Traditional educational practice even fails to encourage students to identify uncertainty when they express knowledge, though mark schemes that reward the identification of reliable and uncertain responses have long been shown to encourage more insightful understanding. In our information-rich society the ability to identify uncertainty is often more important than (...) the possession of knowledge itself. In both science and law there are fundamentally different kinds of uncertainty at issue. There is uncertainty whether a particular hypothesis is correct, and there is uncertainty about observable data that may be generated if a particular hypothesis is correct. Both are expressed in terms of probabilities. Each has its own domain of application and its own logic, but the inter-relationship is complex and sometimes misunderstood. Hypothesis probabilities are always open to error through possible failure to take account of realistic alternatives, while the proper inferences that can be drawn from data probabilities (often in the context of significance testing) are quite limited and easily over-interpreted. When considering these two kinds of probability in a court of law it is possible to interpret the phrase 'reasonable doubt' in different ways. It can be seen as addressing data uncertainty: whether such incriminating evidence might with reasonable probability arise to confront an innocent person. Or (the more conventional view) it can be seen as some sort of threshold level on the probability that the defendant is guilty (a hypothesis probability). Each typically involves elements of subjective judgement, but fewer issues and uncertainties arise when considering the data probability and it is argued that this is often the more critical and proper issue for a jury to address. This has particular repercussions for cases involving identification of a suspect through trawl of a DNA or other database. (shrink)

© The Author 2018. Published by Oxford University Press on behalf of The Analysis Trust. All rights reserved. For Permissions, please email: [email protected] article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model...Philosophy of law was until recently dominated by abstract investigation into the nature of law, a pursuit known as ‘general jurisprudence’. In this way, it resembled a branch of metaphysics or mid-twentieth century philosophy of mind, seeking to uncover the essential properties (...) of a general and multiply realizable phenomenon. Today, philosophers study law with a sharper focus, and with a more specialized lens – that of ethicists, for example, or epistemologists, or philosophers of action.That is not to say there has been a drop in groundbreaking work in general jurisprudence (see, for... (shrink)

The primary aim of this paper is to defend the Lockean View—the view that a belief is epistemically justified iff it is highly probable—against a new family of objections. According to these objections, broadly speaking, the Lockean View ought to be abandoned because it is incompatible with, or difficult to square with, our judgments surrounding certain legal cases. I distinguish and explore three different versions of these objections—The Conviction Argument, the Argument from Assertion and Practical Reasoning, and the Comparative Probabilities (...) Argument—but argue that none of them are successful. I also present some very general reasons for being pessimistic about the overall strategy of using legal considerations to evaluate epistemic theories; as we will see, there are good reasons to think that many of the considerations relevant to legal theorizing are ultimately irrelevant to epistemic theorizing. (shrink)

I argue that the concept of knowledge is a relic of a bygone age, erroneously supposed to do no harm. I illustrate this claim by showing how a concern with knowledge distorts the use of statistical evidence in criminal courts, and then generalize the point to show that this concern hampers our enterprises across the board and not only in legal contexts.

Suppose one hundred prisoners are in a yard under the supervision of a guard, and at some point, ninety-nine of them collectively kill the guard. If, after the fact, a prisoner is picked at random and tried, the probability of his guilt is 99%. But despite the high probability, the statistical chances, by themselves, seem insufficient to justify a conviction. The question is why. Two arguments are offered. The first, decision-theoretic argument shows that a conviction solely based on the statistics (...) in the prisoner scenario is unacceptable so long as the goal of expected utility maximization is combined with fairness constraints. The second, risk-based argument shows that a conviction solely based on the statistics in the prisoner scenario lets the risk of mistaken conviction surge potentially too high. The same, by contrast, cannot be said of convictions solely based on DNA evidence or eyewitness testimony. A noteworthy feature of the two arguments in the paper is that they are not confined to criminal trials and can in fact be extended to civil trials. (shrink)