Decision-theoretic and risk-based approaches to naked statistical evidence: some consequences and challenges
Rafał Urbaniak , Alicja Kowalewska , Pavel Janda , Patryk Dziurosz-Serafinowicz
AbstractIn the debate about the legal value of naked statistical evidence, Di Bello argues that (1) the likelihood ratio of such evidence is unknown, (2) the decision-theoretic considerations indicate that a conviction based on such evidence is unacceptable when expected utility maximization is combined with fairness constraints, and (3) the risk of mistaken conviction based on such evidence cannot be evaluated and is potentially too high. We argue that Di Bello’s argument for (1) works in a rather narrow context, and that (1) is not exactly in line with the way expert witnesses are required to formulate their opinions. Consequently, Di Bello’s argument for (2), which assumes (1), does not apply uniformly to all convictions based on naked evidence. Moreover, if Di Bello’s analysis is correct, it applies also to eyewitness testimony, given empirical results about its quality, and so the distinctions drawn by DiBello cut across the distinction between naked statistical evidence and other types of evidence. Finally, if we weaken the rather strong requirement of precise measurability of the risk of mistaken conviction, to the availability of reasonable but imprecise and fallible estimates, many field and empirical studies show that often the risk of mistaken conviction based on naked statistical evidence can be estimated to a similar extent as the risk of mistaken conviction based on any other sort of evidence.
|Journal series||Law Probability & Risk, [Law, Probability and Risk], ISSN 1470-8396, e-ISSN 1470-840X, (N/A 100 pkt)|
|Publication size in sheets||0.80|
|Keywords in Polish||prawdopodobieństwo, ryzyko, dowody statystyczne|
|Keywords in English||probability, risk, statistical evidence|
|ASJC Classification||; ;|
|Score||= 100.0, 24-02-2020, ArticleFromJournal|
|Publication indicators||: 2016 = 0.870; : 2018 = 0.821 (2) - 2018=1.026 (5)|
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