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A Preliminary Historical Perspective

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Book cover Computer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation

Part of the book series: Law, Governance and Technology Series ((LGTS,volume 5))

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Abstract

This introductory chapter makes considerations about the thematics, the organisation of the book, and (along very broad lines) the state of the art, the latter’s historical development, and its publication forums. The book is organised around three poles: the modelling of reasoning, the modelling of argumentation and its application to narratives, and a cluster of data mining techniques and the specifics of forensic science disciplines. We mention the controversy, among legal scholars, among those willing to accept probabilistic models, and those who want instead a ranking of the relative plausibility of alternative accounts of a legal narrative, without committing to a Bayesian framework. Artificial intelligence is able to contribute to both camps, and has already done so. Bayesian networks are often applied to causality also in the legal domain, but those arguing against probabilistic quantification are at present vindicated by the rise of the plausibility ranking of legal narratives (Section “Bex’s Approach to Combining Stories and Arguments in Sense-Making Software for Crime Investigation”, in Chapter “The Narrative Dimension”, and Section “Another Approach to Critical Questions” in Chapter “Argumentation”) within argumentation research (Chapter “Argumentation”). Artificial Intelligence (AI) practitioners need to exercise care, lest methodological flaws vitiate their tools in the domain with some legal scholars, let alone opponents in litigation. There would be little point for computer scientists to develop tools for legal evidence, if legal scholars would find them vitiated ab initio. This is especially true of tools that would reason about the evidence in criminal cases, in view of fact-finding in the courtroom: whether to convict or not – this being different from the situation of the police, whose aim is to detect crime and to find suspects, without having the duty of proving their guilt beyond reasonable doubt, which is the task of the prosecutors. Tools helping the prosecutor to predict an outcome and choose whether to prosecute are not as central to, and problematic for, the Bayesian controversy, as prescriptive models of judicial decision-making are. This chapter also says something about the communities of users that may benefit from advances in AI & Law technology. In particular, we devote some discussion to computer assistance in policing.

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Notes

  1. 1.

    For evidence in legal scholarship, see, e.g., Twining (1985, 1989, 1990), Stein (2005), Nicolson (1994). A textbook on Evidence in England and Wales is Templeman and Reay (1999). From outside Anglo-Saxon jurisdictions, see, e.g., Tonini (1997).

  2. 2.

    http://elj.warwick.ac.uk/jilt

  3. 3.

    Plausibility may be understood to be either more general than probability, or something different altogether. “Polya developed a formal characterisation of qualitative human reasoning as an alternative to probabilistic methods for performing commonsense reasoning. He identified four patterns of plausible inference: inductive patterns, successive verification of several consequences, verification of improbable consequences and inference from analogy” (Stranieri & Zeleznikow, 2005a, Glossary, s.v. plausible inference).

  4. 4.

    Some readers may feel that throughout this long book, the exposition is somewhat marred by overquotation, but I preferred to take this risk, or rather considered this an acceptable cost: I adopted, in a sense, a documentaristic approach, and often quote verbatim. The scope of the topics touched upon in this book is so vast, as to make it necessary to give readers direct access to some passages in which relevant notions have already been well formulated by other authors.

  5. 5.

    E.g., Robertson and Vignaux (1995); cf. Aitken (1995), Taroni, Aitken, Garbolino, and Biedermann (2006).

  6. 6.

    Also see e.g. Jonathan Cohen’s (1977) The Probable and the Provable.

  7. 7.

    By Dave Schum, see also, e.g., ‘Probability and the processes of discovery, proof, and choice’ (Schum, 1986), and Evidential Foundations of Probabilistic Reasoning (Schum, 1994). On cascaded inference, see Schum and Martin (1982).

  8. 8.

    The statistics of identification of perpetrators from DNA samples is the one area in which the statisticians could be thought, on the face of it, to prevail upon the sceptics, were it not for the contradictions of the several ways in which DNA samples can be interpreted statistically, quite a worrying problem that that has been popularised by Geddes (2010). See Section 8.7.2 below.

  9. 9.

    Another journal special issue on AI & Law, but one in which only part of the papers are on evidence, is Peterson, Barnden, and Nissan (2001). Meanwhile, Kaptein, Prakken, and Verheij have published (2009) the paper-collection Legal Evidence and Proof: Statistics, Stories, Logic.

  10. 10.

    I published about select topics in legal evidence as a challenge for AI in Nissan (2008a). Nissan (2009a) is a survey that served me as a blueprint for the present book. Brief encyclopaedic entries on AI for legal evidence or on computer tools for argumentation that I published include Nissan (2008b, 2008c, 2008d, 2008e). That material is either expanded, or incorporated in the present book.

  11. 11.

    Intelligence led policing is the subject of Ratcliffe (2002, 2003, 2005). Cf. Cope’s article (2004) entitled “Intelligence led policing or policing led intelligence?”. “Where intelligence-led policing differs from other strategies is in the focus on recidivist offenders, and the encouragement given to surveillance and the use of informants to gather intelligence that might not otherwise come to the attention of police […]” (Ratcliffe, 2005, p. 437).

  12. 12.

    We are going to see (in Section 6.1.6.2) that some AI computer tools with a graphic (actually, geographic) interface are intended for training at the police academy, such as ExpertCop, a tool developed by Vasco Furtado’s team in Brazil (Furtado & Vasconcelos, 2007).

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Authors and Affiliations

  1. Department of Computing, Goldsmiths’ College, University of London, London, SE14 6NW, England

    Dr. Ephraim Nissan

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  1. Dr. Ephraim Nissan
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Correspondence to Ephraim Nissan .

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Nissan, E. (2012). A Preliminary Historical Perspective. In: Computer Applications for Handling Legal Evidence, Police Investigation and Case Argumentation. Law, Governance and Technology Series, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8990-8_1

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