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Normative decision analysis in forensic science

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Abstract

This paper focuses on the normative analysis—in the sense of the classic decision-theoretic formulation—of decision problems that arise in connection with forensic expert reporting. We distinguish this analytical account from other common types of decision analyses, such as descriptive approaches. While decision theory is, since several decades, an extensively discussed topic in legal literature, its use in forensic science is more recent, and with an emphasis on goals such as the analysis of the logical structure of forensic expert conclusions regarding, for example, propositions of common source of evidential and known materials. Typical examples are so-called identification (or, individualization) decisions, especially categorical conclusions according to which fingermarks (or stains of biological nature, handwriting, etc.) come from a particular a person of interest. We will present and compare ways of stating forensic identification decisions in decision-theoretic terms and explain their underlying rationale. In particular, we will emphasize the importance of viewing this analysis as normative in the sense of providing a reflective rather than a prescriptive reference point against which people in charge of forensic identification decisions may compare their otherwise (possibly) intuitive and informal reasoning, before acting. Normative decision analysis in forensic science thus provides a vector through which current practice can be articulated, scrutinized and rethought.

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Notes

  1. An illustrative example for this is the recent ICAIL 2017 workshop Evidence & Decision Making in the Law—Theoretical, Computational and Empirical Approaches (The 16th International Conference on Artificial Intelligence and Law, June 16 2017, King’s College, London) which covered contributions to help make progress in our understanding of the following topics:

    1. “1.

      Modeling evidential reasoning and decision making at trial (e.g. evidence weighing; conflict resolution; standards of proof and rules of decision);

    2. 2.

      Evidence-based decision making and the architecture of the trial system (e.g. rules of admissibility; discovery procedures; adversary v. inquisitorial models; rules of weight v. free proof); and

    3. 3.

      The role and limitations of expected utility theory, and more generally cost/benefit analysis, for evidence-based decision making.”*

    *https://icail2017evidencedecision.wordpress.com/cfp/ (last accessed Aug 31 2018).

  2. We use here the term expected value theory in a generic sense, as a synonym for classic decision theory, introduced in a non-technical way in Sect. 2. It is understood that the term ‘value’ may be replaced, for example, by the notions of utility or loss, depending on the area of application.

  3. In forensic science, the term individualization is commonly understood as the reduction of a pool of potential donors of a forensic trace (e.g., an impression, a mark or physical matter) to a single source, which may be an object or a person (Champod 2000).

  4. Non-identification is understood here not as ‘exclusion’, but as an unspecified alternative covering all situations other than ‘identification’.

  5. It is assumed here that this probability is also informed by any relevant evidence E bearing on H that may be available to the decision-maker at the time of the decision. Thus, one’s decision is preceded by inference based on available evidence. This inferential step, commonly dealt with probabilistically (Aitken and Taroni 2004), is not covered here, and \(\Pr (H_{1}{\mid }E, I)\) is written more shortly as \(\Pr (H_{1}{\mid }I)\).

  6. Note that the provision of a norm does not imply prescription. See also Sect. 5.2 on the notion of conditional advice.

  7. Note that this is different for the minimax criterion mentioned in Sect. 4.1 which involves no computation, only a direct comparison between losses of decision consequences.

  8. It is possible to understand this term as the prior odds or the posterior odds for the propositions of interest. The latter is the product of the prior odds and the likelihood ratio for any data considered relevant (e.g., Parmigiani 2001).

  9. It is also possible to interpret the magnitudes as the probabilities and the length of the levers as the losses of adverse decision consequences. See Biedermann et al. (2016a) for further discussion.

  10. The value of empowering individual decision-makers in various societal positions and functions is also recognised in areas beyond the law. An example for this in philosophy of politics is Mondadori who has been quoted as saying “[w]e can say that—given the importance of decisions in one’s every day life—it is a matter of democracy to have the opportunity to be supported by logical mechanisms for formal analysis of information.” (D’Agostino et al. 2001, p. 11)

  11. Note that SWGFAST evolved to what currently is the Subcommittee on Friction Ridge, which is part of the Organization of Scientific Area Committees (OSAC), administered by National Institute of Standards and Technology (NIST).

  12. The term ‘decision’ is also systematically used in a recent report issued by the AAAS (Thompson et al. 2017).

  13. The ‘value of findings’ is also sometimes referred to as the ‘weight of evidence’, that is an expression of the way and the extent to which particular (scientific) results help to discriminate between competing propositions of interest (e.g., Willis et al. 2015).

  14. Note that the quote from the DFSC announcement considers the terms likelihood and probability as synonyms which, strictly speaking, is not correct from a statistical point of view.

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Acknowledgements

Alex Biedermann gratefully acknowledges the support of the Swiss National Science Foundation through Grant No. BSSGI0_155809 and the University of Lausanne. Some of the writing of this paper was carried out during a visiting research stay of Alex Biedermann at New York University School of Law.

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Biedermann, A., Bozza, S. & Taroni, F. Normative decision analysis in forensic science. Artif Intell Law 28, 7–25 (2020). https://doi.org/10.1007/s10506-018-9232-2

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