Trial by Statistics: Is a High Probability of Guilt Enough to Convict?
Mind , Forthcoming
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.
Can Probability Theory Explain Why Closure Is Both Intuitive and Prone to Counterexamples? ×
Philosophical Studies , 175(9): 2018
Epistemic closure under known implication is the principle that knowledge of A and knowledge of "if A, then B", together, imply knowledge of B. This principle is intuitive, yet several putative counterexamples have been formulated against it. This paper addresses the question, why is epistemic closure both intuitive and prone to counterexamples? In particular, the paper examines whether probability theory can offer an answer to this question based on four strategies. The first probability-based strategy rests on the accumulation of risks. The problem with this strategy is that risk accumulation cannot accommodate certain counterexamples to epistemic closure. The second strategy is based on the idea of evidential support, that is, a piece of evidence supports a proposition whenever it increases the probability of the proposition. This strategy makes progress and can accommodate certain putative counterexamples to closure. However, this strategy also gives rise to a number of counterintuitive results. Finally, there are two broadly probabilistic strategies, one based on the idea of resilient probability and the other on the idea of assumptions that are taken for granted. These strategies are promising but are prone to some of the shortcomings of the second strategy. All in all, I conclude that each strategy fails. Probability theory, then, is unlikely to offer the account we need.
Epistemic Closure, Assumptions and Topics of Inquiry×
Synthese , 191(16): 2014
According to the principle of epistemic closure, knowledge is closed under known implication. The principle is intuitive but it is problematic in some cases. Suppose you know you have hands and you know that 'I have hands' implies 'I am not a brain-in-a-vat'. Does it follow that you know you are not a brain-in-a-vat? It seems not; it should not be so easy to refute skepticism. In this and similar cases, we are confronted with a puzzle: epistemic closure is an intuitive principle, but at times, it does not seem that we know by implication. In response to this puzzle, the literature has been mostly polarized between those who are willing to do away with epistemic closure and those who think we cannot live without it. But there is a third way. Here I formulate a restricted version of the principle of epistemic closure. In the standard version, the principle can range over any proposition; in the restricted version, it can only range over those propositions that are within the limits of a given epistemic inquiry and that do not constitute the underlying assumptions of the inquiry. If we adopt the restricted version, I argue, we can preserve the advantages associated with closure, while at the same time avoiding the puzzle I've described. My discussion also yields an insight into the nature of knowledge. I argue that knowledge is best understood as a topic-restricted notion, and that such a conception is a natural one given our limited cognitive resources.
- Trial by Statistics: Is a High Probability of Guilt Enough to Convict? ×
Evidential Reasoning ×
In Bongiovanni, Postema, Rotolo, Sartor, Valentini and Walton (eds). Handbook of Legal Reasoning and Argumentation , Springer, 2018
The primary aim of this chapter is to explain the nature of evidential reasoning, the characteristic difficulties encountered, and the tools to address these difficulties. Our focus is on evidential reasoning in criminal cases. There is an extensive scholarly literature on these topics, and it is a secondary aim of the chapter to provide readers the means to find their way in historical and ongoing debates.
Betraying Davidson: A Quest for the Incommensurable×
In Carrara and Morato (eds). Language, Knowledge, and Metaphysics , College Publications, 2009
Talks of incommensurable conceptual schemes typically allude to the following picture: there is reality that is inaccessible per se, on the one hand, and there is us, or different groups of people, accessing reality by means of incommensurable conceptual schemes, on the other. But what does it mean for two schemes to be incommensurable? And is incommensurability at all intelligible to us? Donald Davidson famously argued that incommensurability is unintelligible. His argument first provides a definition of incommensurability, and then shows that incommensurability thus defined is unintelligible. I will argue, contra Davidson, that his definition of incommensurability does not lend any support to the unintelligibility claim.
LKIF Core : Principled Ontology Development for the Legal Domain×
In Breuker, Casanovas, Klein and Francesconi (eds). Law, Ontology, and the Semantic Web , IOS Press, 2009 (with Hoekstra, Breuker, and Boer)
In this paper we describe a legal core ontology that is part of the Legal Knowledge Interchange Format: a knowledge representation formalism that enables the translation of legal knowledge bases written in different representation formats and formalisms. A legal (core) ontology can play an important role in the translation of existing legal knowledge bases to other representation formats, in particular as the basis for articulate knowledge serving. This requires that the ontology has a firm grounding in commonsense and is developed in a principled manner. We describe the theory and methodology underlying the LKIF core ontology, compare it with other ontologies, introduce the concepts it defines, and discuss its use in the formalisation of an EU directive.
- Evidential Reasoning ×
Review of Walton's Argument Evaluation and Evidence ×
Argumentation , 32(3): 2018 (with Bart Verheij)
- Review of Walton's Argument Evaluation and Evidence ×
Statistics and Probability in Criminal Trials: The Good, the Bad, and the Ugly×
Ph.D. Thesis, Stanford University, 2013
Is a high probability of guilt, in and of itself, enough to convict? I maintain that the correct answer is No. I argue that the prosecutor's burden of proof does not only consist in establishing the high probability of the defendant's guilt; it also consists in (1) establishing guilt with a resiliently high probability, and in (2) offerring a reasonably specific and detailed narrative of the crime. My account has applications to debates in epistemology about lottery propositions, and to questions in legal scholarship regarding the use of statistical evidence in criminal trials.
Formalizing Legislation in the Event Calculus: The Case of the Italian Citizenship Law×
M.Sc. Thesis, University of Amsterdam (ILLC), 2007
I explores to what extent legal knowledge and legal reasoning can be encoded in the Event Calculus and how the Event Calculus needs to be extended (if at all) to accommodate legal reasoning and knowledge. My working hypothesis is that a considerable portion of legal knowledge and reasoning is contained in legislative texts (laws, decrees, regulations, directives, etc.). So my primary task will be to formalize a piece of legislation in the language of the Event Calculus.
- Statistics and Probability in Criminal Trials: The Good, the Bad, and the Ugly×
Critical Reasoning ×
Argument analysis; probability theory; formulating competing hypotheses; collecting evidence; Weghing evidence with probability theory.
- Critical Reasoning ×
Is the Criminal Justice System Racist? ×
Examination of how and to what extent the US criminal justice system is racist.
Legal Reasoning ×
Fall 2017; Fall 2015
Examination of how judges and lawyers reason, in particular, how legal rules apply to individual cases; how judges interpret the text of the law; how conclusions about “matters of fact” are reached in court; and how past court decisions influence current decisions.
- Problems of philosophy ×
Philosophy of Law/Jurisprudence ×
Spring 2016; Spring 2015
What makes a written text a piece of law? How should judges decide difficult cases? Why should we obey the law? Should we obey immoral laws? How many errors does the justice system make? What is a tolerable margin of error?
Critical Reasoning ×
Fall 2014; Spring 2016
Introduction to the concepts and methods of thinking, reading and writing analytically.
Convicting the Innocent ×
Adversarial v. inquisitorial trials; casues of mistaken convictions; remedies; modelling error and truth in the criminal trial.
Math on Trial ×
Uses and abuses of statistics and probability in the criminal trial; examination of infamous cases, such as Collins and Lucia de Berk; cold-hit DNA matches.
Introduction to Logic ×
Syntax and semantics of propositional and predicate logic; modal and inductive logic.
Probability and the Law ×
Winter 2014; Winter 2013
What does it mean to prove guilt beyond a reasonable doubt? Can we interpret legal standards of proof probabilistically? What is the role of probability and statistics in the courtroom? How are quantitative methods changing legal proceedings? No statistical or legal background is expected.
Introduction to Philosophy ×
Questions about the essence of reality; the limits of knowledge; happiness and a good life; mind and body; free will and determinism.
Introduction to Statistics ×
Basic statistical concepts. Uses, misuses and limitations of statistical methods. Emphasis on the concepts rather than the computations. No mathematical background is needed, except high school algebra and willingness to think carefully.
Modal Logic and its Applications×
Introduction to modal logic and its applications to metaphysics, epistemology, formal semantics and artificial intelligence. No previous knowledge of logic or philosophy is expected.
- Is the Criminal Justice System Racist? ×