I defend the thesis that legal standards of proof are reducible to thresholds of probability. Many reject this thesis because it appears to permit finding defendants liable solely on the basis of statistical evidence. To the contrary, I argue – by combining Thomson's (1986) causal analysis of legal evidence with formal methods of causal inference – that legal standards of proof can be reduced to probabilities, but that deriving these probabilities involves more than just statistics.

ABSTRACT Rima Basu and I have offered separate accounts of wokeness as an anti-racist ethical concept. Our accounts endorse controversial doctrines in epistemology: doxastic wronging, doxastic voluntarism, and moral encroachment. Many philosophers deny these three views, favoring instead some ordinary standards for epistemic justification. I call this denial the standard view. In this paper, I offer an account of wokeness that is consistent with the standard view. I argue that wokeness is best understood as ‘group epistemic partiality’. The woke person (...) does extra epistemic work before forming a negative belief about a member of an oppressed social group. Just as we do extra epistemic work when forming belief about our friends, so the woke person does for members of oppressed social groups. I first outline the account. I then raise questions about the scope of wokeness and belief formation. After this, I demonstrate that the group partiality view is consistent with the standard view in epistemology. The partiality view, therefore, should appeal to epistemologists who have adopted the standard view because it is consistent with ordinary standards of justification. I conclude that wokeness as a concept in epistemology should not be controversial for those who endorse the standard view. (shrink)

The notion of individualised evidence holds the key to solve the puzzle of statistical evidence, but there’s still no consensus on how exactly to define it. To make progress on the problem, epistemologists have proposed various accounts of individualised evidence in terms of causal or modal anti-luck conditions on knowledge like appropriate causation, sensitivity and safety. In this paper, I show that each of these fails as satisfactory anti-luck condition, and that such failure lends abductive support to the following conclusion: (...) once the familiar anti-luck intuition on knowledge is extended to individualised evidence, no single causal or modal anti-luck condition on knowledge can succeed as the right anti-luck condition on individualised evidence. This conclusion casts serious doubts on the fruitfulness of the move from anti-luck conditions on knowledge to anti-luck conditions on individualised evidence. I expand on these doubts and point out further aspects where epistemology and the law come apart: epistemic anti- luck conditions on knowledge do not adequately characterise the legal notion of individualised evidence. (shrink)

This essay presents a unified account of safety, sensitivity, and severe testing. S’s belief is safe iff, roughly, S could not easily have falsely believed p, and S’s belief is sensitive iff were p false S would not believe p. These two conditions are typically viewed as rivals but, we argue, they instead play symbiotic roles. Safety and sensitivity are both valuable epistemic conditions, and the relevant alternatives framework provides the scaffolding for their mutually supportive roles. The relevant alternatives condition (...) holds that a belief is warranted only if the evidence rules out relevant error possibilities. The safety condition helps categorise relevant from irrelevant possibilities. The sensitivity condition captures ‘ruling out’. Safety, sensitivity, and the relevant alternatives condition are typically presented as conditions on warranted belief or knowledge. But these properties, once generalised, help characterise other epistemic phenomena, including warranted inference, legal verdicts, scientific claims, reaching conclusions, addressing questions, warranted assertion, and the epistemic force of corroborating evidence. We introduce and explain Mayo’s severe testing account of statistical inference. A hypothesis is severely tested to the extent it passes tests that probably would have found errors, were they present. We argue Mayo’s account is fruitfully understood using the resulting relevant alternatives framework. Recasting Mayo’s condition using the conceptual framework of contemporary epistemology helps forge fruitful connections between two research areas—philosophy of statistics and the analysis of knowledge—not currently in sufficient dialogue. The resulting union benefits both research areas. (shrink)

Philosophical Issues, Volume 31, Issue 1, Page 355-373, October 2021.

The remaining seven papers (eight, if you count this introductory piece) in this volume of Oxford Studies in Epistemology constitute a special issue on applied epistemology, an exciting, novel, and currently burgeoning subfield of epistemology. The term ‘applied epistemology’ is a relatively recent one, however, and anecdotally, many people I’ve encountered are not quite sure what it denotes, or what different works within the field have in common. In this introductory piece, I’ll venture some views about these questions, and about (...) why applied epistemology is worth doing, as well as about its dangers. Doing so will set the state for me to situate the papers in this volume within the subfield. (shrink)

Many philosophers have pointed out that statistical evidence, or at least some forms of it, lack desirable epistemic or non-epistemic properties, and that this should make us wary of litigations in which the case against the defendant rests in whole or in part on statistical evidence. Others have responded that such broad reservations about statistical evidence are overly restrictive since appellate courts have expressed nuanced views about statistical evidence. In an effort to clarify and reconcile, I put forward an interpretive (...) analysis of why statistical evidence should raise concerns in some cases but not others. I argue that when there is a mismatch between the specificity of the evidence and the expected specificity of the accusation, statistical evidence—as any other kind of evidence—should be considered insufficient to sustain a conviction. I rely on different stylized court cases to illustrate the explanatory power of this analysis. (shrink)

Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, problems arise for using safety as a condition on knowledge: safety is not necessary for knowledge and cannot always explain the Gettier cases and cases of statistical evidence it is meant to address. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence required for knowledge: (...) causal safety. I argue that possible errors in belief can be captured by accounting for deviations in causal relationships and that there is a natural way to characterize which causal errors are relevant in an epistemic situation. Using this, I develop a causal analogue to safety, where one’s belief in _p_ is causally safe if it is true in all causally relevant worlds where one believes _p_. Causal safety, I argue, can better explain the cases safety is meant to address and can avoid the arguments raised against the necessity of safety. (shrink)

Philosophical Issues, Volume 31, Issue 1, Page 146-166, October 2021.

Corroborating evidence supports a proposition that is already supported by other initial evidence. It bolsters or confirms the original body of evidence. Corroboration has striking psychological and epistemic force: It potently affects how people do and should assess the target proposition. This essay investigates the distinctive powers of corroborating evidence. Corroboration does not simply increase the quantifiable probability of the adjudicated claim. Drawing on the relevant alternatives framework, I argue that corroboration winnows remaining uneliminated error possibilities. This illuminates the independence, (...) weight, and non-fungibility of corroborating evidence. I compare corroborating evidence to prudential safeguards, like fire doors, that forfend against non-epistemic harms. I thereby sketch a general, non-quantificational model of risk management. Finally, I turn to legal corroboration requirements and the epistemic significance of corroboration for legal proof. (shrink)

Epistemologists have posed the following puzzle, known as the proof paradox: Why is it intuitively problematic for juries to convict on the basis of statistical evidence and yet intuitively unproblematic for juries to convict on the basis of far less reliable, non-statistical evidence? To answer this question, theorists have explained the exclusion of statistical evidence by arguing that legal proof requires certain epistemic features. In this paper, I make two contributions to the debate. First, I establish the Criminal Verdict Asymmetry, (...) a previously-unarticulated asymmetry between the epistemic norms of guilty and not guilty verdicts. I argue that the prosecution and defense’s different epistemic burdens influence whether statistical evidence can generate the type of verdict each side pursues. Second, I point out a mistake in how theorists have understood the role of statistical evidence in criminal trials. Though epistemologists have primarily focused on whether statistical evidence can generate specific epistemic features required for convictions, I consider whether statistical evidence can demonstrate a lack of such features. I find that there are epistemic advantages to allowing the defense to introduce statistical evidence which can reveal the prosecution’s failure to prove the defendant’s guilt. (shrink)