Skip to main content
Log in

Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement

  • Open Forum
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

‘AI & Law’ research has been around since the 1970s, even though with shifting emphasis. This is an overview of the contributions of digital technologies, both artificial intelligence and non-AI smart tools, to both the legal professions and the police. For example, we briefly consider text mining and case-automated summarization, tools supporting argumentation, tools concerning sentencing based on the technique of case-based reasoning, the role of abductive reasoning, research into applying AI to legal evidence, tools for fighting crime and tools for identification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aggarwal CC (2011) Social network data analysis. Springer, Berlin

    Book  MATH  Google Scholar 

  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases (VLDB’94), Santiago, Chile, pp 487–99

  • Akin L (2012) Effects of velocity on blood drops and blood spatter, in Nissan (2012), Sec. 8.8.5

  • Aleven V, Ashley KD (1997) Evaluating a learning environment for case-based argumentation skills. In: Proceedings of the sixth international conference on artificial intelligence and law. ACM Press, New York, pp 170–179

  • Alheit K (1989) Expert systems in law: issues of liability, in Martino (1989), vol 2, pp 43–52

  • Allen RJ (1991) The nature of juridical proof. Cardozo Law Review 13:373–422

    Google Scholar 

  • Allen RJ (1994) Factual ambiguity and a theory of evidence. Northeastern University Law Review 88:604–640

    Google Scholar 

  • Allen RJ (2008a) Explanationism all the way down. Episteme 3(5):320–328

    Article  Google Scholar 

  • Allen RJ (2008b) Juridical proof and the best explanation. Law & Philosophy 27:223–268

    Article  Google Scholar 

  • Allen RJ, Pardo MS (2007) The problematic value of mathematical models of evidence. Journal of Legal Studies 36:107–140

    Article  Google Scholar 

  • Allen RJ, Pardo MS (2008) Juridical proof and the best explanation. Law & Philosophy 27:223–268

    Article  Google Scholar 

  • Allen R, Redmayne M (eds) (1997) In: Bayesianism and juridical proof, special issue of the International Journal of Evidence and Proof, 1 (Blackstone, London), pp 253–360

  • Anderson T, Schum D, Twining W (2005) Analysis of evidence: how to do things with facts. Based on Wigmore’s science of judicial proof. Cambridge University Press, Cambridge, UK

    Book  Google Scholar 

  • Åqvist L (1984) Deontic logic. In: Gabbay D, Guenthner F (eds) Handbook of Philosphical Logic, Extensions of classical logic, vol 2. Reidel (Kluwer, now Springer), Dordrecht, pp 605–714

    Google Scholar 

  • Åqvist L (1986) Introduction to Deontic Logic and the Theory of Normative Systems (Indices. Monographs in Philosophical Logic and Formal Linguistics, 4). Bibliopolis, Naples, Italy

  • Åqvist L (1992) Towards a logical theory of legal evidence: Semantic analysis of the Bolding–Ekelöf degrees of evidential strength. In: Martino AA (ed) Expert systems in law. North-Holland, Amsterdam, pp 67–86

    Google Scholar 

  • Aron J (2012) CSI: augmented reality could help solve murders. In: New Scientist (UK edition), vol 213(2849), London, 28 Jan 2012, p 23

  • Asaro C (2012) Ingegneria della conoscenza giuridica applicata al diritto penale. Aracne, Rome

    Google Scholar 

  • Asaro C, Nissan E, Martino AA (2012) Daedalus, a procedural-support tool for the italian examining magistrate and prosecutor, in Nissan (2012), Sec. 4.1.3

  • Ashley K (1991) Modeling legal argument: reasoning with cases and Hypotheticals. The MIT Press (Bradford Books), Cambridge, MA

    Google Scholar 

  • Baber C (2010) Distributed cognition at the crime scene. AI & Society 25:423–432

    Article  Google Scholar 

  • Badiru AB, Karasz JM, Holloway RT (1988) Arest: [sic] armed robbery eidetic suspect typing expert system. Journal of Police Science and Administration 16(3):210–216

    Google Scholar 

  • Bain WM (1986) Case-based reasoning: a computer model of subjective assessment, Ph.D. thesis. Computer Science Department, Yale University, New Haven, CT

  • Bain WM (1989a) Judge. In: Riesbeck CK, Schank RC (eds) Inside case-based reasoning. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 93–140

    Google Scholar 

  • Bain WM (1989b) Microjudge. In: Riesbeck CK, Schank RC (eds) Inside case-based reasoning. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 141–163

    Google Scholar 

  • Ball WJ (1994) Using Virgil to analyse public policy arguments: a system based on Toulmin’s informal logic. Social Science Computer Review 12(1):26–37

    Article  Google Scholar 

  • Barnden JA (2001) Uncertain reasoning about agents’ beliefs and reasoning. Artificial Intelligence and Law 9(2/3):115–152

    Article  Google Scholar 

  • Basta S, Giannotti F, Manco G, Pedreschi D, Spisanti L (2009) SNIPER: a data mining methodology for fiscal fraud detection. In: Mathematics for Finance and Economy, special issue of ERCIM News, 78 (July), pp 27–28

  • Bayse WA, Morris CG (1987) FBI automation strategy: development of AI applications for national investigative programs, Signal Magazine, May

  • Beecher-Monas E (2008) Paradoxical validity determinations: a decade of antithetical approaches to admissibility of expert evidence, International Commentary on Evidence 6(2), Article 2. http://www.bepress.com/ice/vol6/iss2/art2

  • Bench-Capon TJM (1998) Specification and implementation of Toulmin dialogue game. In: Hage JC, Bench-Capon T, Koers A, de Vey Mestdagh C, Grutters C (eds) Jurix 1998: foundation for legal knowledge based systems. Gerard Noodt Institut, Nijmegen, Netherlands, pp 5–20

    Google Scholar 

  • Bex FJ (2011) Arguments, stories and criminal evidence: a formal hybrid theory (law and philosophy series, 92). Springer, Dordrecht

    Book  Google Scholar 

  • Bex FJ, van den Braak SW, van Oostendorp H, Prakken H, Verheij HB, Vreeswijk GAW (2007) Sense-making software for crime investigation: how to combine stories and arguments? Law, Probability & Risk 6, pp 145–68. http://www.computing.dundee.ac.uk/staff/florisbex/Papers/LPR07.pdf. The same article with diagrams in colour: http://www.cs.uu.nl/research/projects/evidence/publications/lpr07submitted.pdf

  • Bex F, Bench-Capon T, Atkinson K (2009) Did he jump or was he pushed? Abductive practical reasoning, Artificial Intelligence and Law, 17(2), pp 79–99. http://www.computing.dundee.ac.uk/staff/florisbex/Papers/AILaw09.pdf

  • Blackman SJ (1988) Expert systems in case-based law: the rule against hearsay. LL.M. thesis, Faculty of Law, University of British Columbia, Vancouver, Canada

  • Boba R (2005) Crime Analysis and crime mapping. Sage, Thousand Oaks, CA

    Google Scholar 

  • Bolding PO (1960) Aspects of the burden of proof, Scandinavian Studies in Law (Faculty of Law, Stockholm University), 4, pp 9–28

  • Brandes U, Raab J, Wagner D (2001) Exploratory network visualization: simultaneous display of actor status and connections, Journal of Social Structure 2(4). http://www.cmu.edu/joss/content/articles/volume2/BrandesRaabWagner.html

  • Bromby M (2010) Identification, trust and privacy: how biometrics can aid certification of digital signatures. International Review of Law, Computers and Technology 24(1):1–9

    Article  Google Scholar 

  • Bromby MC, Hall MJJ (2002) The development and rapid evaluation of the knowledge model of ADVOKATE: an advisory system to assess the credibility of eyewitness testimony. In: Bench-Capon T, Daskalopulu A, Winkels R (eds) Legal Knowledge and Information Systems, JURIX 2002: the fifteenth annual conference. IOS Publications, Amsterdam, pp 143–152

    Google Scholar 

  • Bromby M, MacMillan M, McKellar P (2003) A common-KADS representation for a knowledge based system to evaluate eyewitness identification. International Review of Law Computers and Technology 17(1):99–108

    Article  Google Scholar 

  • Bromby M, MacMillan M, McKellar P (2007) An examination of criminal jury directions in relation to eyewitness identification in commonwealth jurisdictions. Common Law World Review 36(4):303–336

    Article  Google Scholar 

  • Carr CS (2003) Using computer supported argument visualization to teach legal argumentation. In: Kirschner PA, Buckingham Shum SJ, Carr CS (eds) Visualizing argumentation: software tools for collaborative and educational sense-making. Springer, London, pp 75–96

    Chapter  Google Scholar 

  • Chan H, Lee R, Dillon T, Chang E (2001) E-commerce: fundamentals and applications. Wiley, Chichester, UK

    Google Scholar 

  • Chau DH, Pandit S, Faloutsos C (2006) Detecting fraudulent personalities in networks of online auctioneers. In: Proceedings of the European conference on machine learning (ECM) and Principles and practice of knowledge discovery in databases (PKDD) 2006. Berlin, 18–22 Sept 2006, pp 103–114

  • Chau DH, Nachenberg C, Wilhelm J, Wright A, Faloutsos C (2010) Polonium: tera-scale graph mining for malware detection. In: Proceedings of the 2nd workshop on large-scale data mining: theory and applications (LDMTA 2010), Washington, DC, 25 July 2010. http://www.ml.cmu.edu/current_students/DAP_chau.pdf

  • Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining. IEEE Computer 37(4):50–56

    Article  Google Scholar 

  • Choueka Y (1989) Responsa, a full-text retrieval system with linguistic processing for a 65 million-word corpus of Jewish heritage in Hebrew. In: Special issue on non-english interfaces to databases, data engineering (IEEE Computer Society), 12(4), pp 22–31

  • Choueka Y, Cohen M, Dueck J, Fraenkel AS, Slae M (1971) Full-text document retrieval: Hebrew legal texts (report on the first phase of the Responsa Retrieval Project). In: Minker M, Rosenfeld S (eds) Proceedings of the ACM symposium on information storage and retrieval (Maryland, 1971). Association for Computing Machinery, New York, pp 61–79

    Google Scholar 

  • Choueka, Y., Cohen, M., Dueck, J., Fraenkel, A.S., and Slae, M. (1972), Full-text law retrieval: the responsa project (working papers on legal information processing series, 3), J. Schweitzer Verlag, Berlin, p 64 (a solicited expanded form of the preceding paper)

  • Choueka Y, Slae M, Schreiber A (1980) The responsa project: computerization of traditional Jewish law, Ch. 18. In: Erez B (ed) Legal and legislative information processing. Greenwood Press, New York, pp 261–286

    Google Scholar 

  • Ciampolini A, Torroni P (2004) Using abductive logic agents for modelling judicial evaluation of criminal evidence. Applied Artificial Intelligence 18(3/4):251–275

    Article  Google Scholar 

  • Coady WF (1985) Automated link analysis: artificial intelligence-based tool for investigators. Police Chief 52(9):22–23

    Google Scholar 

  • Combrink-Kuiters CJM, De Mulder RV, van Noortwijk C (2000) Jurimetrical research on judicial decision-making: a review, at Intelligent decision support for legal practice (IDS 2000). In: Proceedings of the international ICSC congress “Intelligent systems and applications” (ISA 2000), Wollongong, New South Wales, Australia, December 2000. Wetaskiwin, ICSC Academic Press, Alberta, Canada, 2000, Vol. 1, pp 109–117

  • Console L, Torasso P (1991) A spectrum of logical definitions of model-based diagnosis. Computational Intelligence 7(3):133–141

    Article  Google Scholar 

  • Daniels JJ, Rissland EL (1997) Finding legally relevant passages in case opinions. In: Proceedings of the sixth international conference on artificial intelligence and law. Melbourne, Australia. ACM Press, New York, pp 39–46

  • de Kleer J (1986) An assumption-based TMS. Artificial Intelligence 28:127–162

    Article  Google Scholar 

  • de Kleer J (1988) A general labeling algorithm for assumption-based truth maintenance. In: Proceedings of the 7th national conference on artificial intelligence, pp 188–192

  • Dyer MG (1983) In-depth understanding: a computer model of integrated processing of narrative comprehension. The MIT Press, Cambridge, MA

    Google Scholar 

  • Ebert LC, Ruder T, Zimmermann D, Zuber S, Buck U, Roggo A, Thali M, Hatch G (2012) Virtopsy: the virtual autopsy, in Nissan (2012), Chapter 9

  • Egeland T, Mostad P, Olaisen B (1997), A computerised method for calculating the probability of pedigrees from genetic data. Science & Justice, 37(4), pp 269–274 (cf. http://www.nr.no/~mostad/pater)

  • Ekelöf PO (1964) Free evaluation of evidence. Scandinavian Studies in Law (Faculty of Law, Stockholm University), 8, pp 45–66

  • Fakher-Eldeen F, Kuflik T, Nissan E, Puni G, Salfati R, Shaul Y, Spanioli A (1993) Interpretation of imputed behaviour in ALIBI (1 to 3) and skill. Informatica e Diritto (Florence), Year 19, 2nd Series, 2(1/2), pp 213–242

  • Falkenhainer B, Forbus K (1991) Compositional modeling: finding the right model for the job. Artificial Intelligence 51:95–143

    Article  Google Scholar 

  • Farzindar A, Lapalme G (2004) Legal texts summarization by exploration of the thematic structures and argumentative roles. In: Text summarization branches out conference held in conjunction with The Association for Computational Linguistics 2004, Barcelona, Spain, July 2004. http://www.iro.umontreal.ca/~farzinda/FarzindarAXL04/pdf

  • Fenton NE, Neil M (2000), The jury observation fallacy and the use of Bayesian networks to present probabilistic legal arguments. Mathematics today: bulletin of the Institute of Mathematics and its Application (IMA), 36(6), 180–187. http://www.agena.co.uk/resources.html

  • Finkelstein MO, Levin B (2003) On the probative value of evidence from a screening search. Jurimetrics Journal 43:265–290

    Google Scholar 

  • Freeman K, Farley AM (1996) A model of argumentation and its application to legal reasoning. Artificial Intelligence and Law 4(3/4):157–161

    Google Scholar 

  • Fu X, Boongoen T, Shen Q (2010) Evidence directed generation of plausible crime scenarios with identity resolution. Applied Artificial Intelligence 24(4):253–276

    Article  Google Scholar 

  • Furtado V, Vasconcelos E (2007) Geosimulation in education: a system for teaching police resource allocation. International Journal of Artificial Intelligence in Education 17:57–81

    Google Scholar 

  • Gelbart D, Smith JC (1993) FLEXICON: an evaluation of a statistical model adapted to intelligent text management. In: Proceedings of the fourth international conference on artificial intelligence and law (ICAIL’93), Amsterdam. ACM Press, New York, pp 142–151

  • Gilbreth FB, Gilbreth LM (1917) Applied motion study. Sturgis and Walton, New York

    Google Scholar 

  • Goble L (1999) Deontic logic with relevance. In: McNamara P, Prakken H (eds) Norms, logics and information systems. ISO Press, Amsterdam, pp 331–346

    Google Scholar 

  • Goldberg HG, Wong RWH (1998) Restructuring transactional data for link analysis in the FinCEN AI system. In Jensen D, Goldberg H (eds) Artificial intelligence and link analysis: papers from the AAAI fall symposium, Orlando, Florida

  • Gordon TF, Walton D (2006) The Carneades argumentation framework: using presumptions and exceptions to model critical questions, at The sixth international workshop on computational models of natural argument, held together with ECAI’06, Riva del Garda, Italy, August 2006

  • Grover C, Hachey B, Hughson I, Korycinski C (2003) Automatic summarisation of legal documents. In: Proceedings of the ninth international conference on artificial intelligence and law (ICAIL 2003), Edinburgh, Scotland. ACM Press, New York, pp 243–251

  • Gulotta G, Zappalà A (2001) The conflict between prosecution and defense in a child sexual abuse case and in an attempted homicide case. In: Peterson DM, Barnden JA, Nissan E (eds) Artificial intelligence and law, special issue of Information and Communications Technology Law, 10(1), pp 91–108

  • HaCohen-Kerner Y, Schild UJ (1999) The judge’s apprentice. In: Knight B, Nissan E (eds) Forum on Case-Based Reasoning, thematic section in the New Review of Applied Expert Systems, vol 5, pp 191–202

  • Hahn U, Mani I (2000) The challenges of automatic summarization. IEEE Computer 33(11):29–36

    Article  Google Scholar 

  • Hamill JT (2006) Analysis of layered social networks, Ph.D. dissertation, Report AFIT/DS/ENS/06 03, Graduate School of Engineering and Management, Air Force Institute of Technology (Air University). http://www.afit.edu/en/docs/ENS/dissertations/Hamill.pdf. http://www.au.af.mil/au/awc/awcgate/afit/hamill_layered_social_networks.pdf

  • Hamkins JD, Löwe B (2008) The modal logic of forcing. Transactions of the American Mathematical Society 360:1793–1817

    Article  MathSciNet  MATH  Google Scholar 

  • Harbert T (2012) Lex Machina arms corporate leaders and patent attorneys with predictive analytics. DATAINFORMED, 6 June 2012. http://data-informed.com/lex-machina-arms-corporate-leaders-and-patent-attorneys-with-predictive-analytics/

  • Hartley JRM, Varley G (2001) The design and evaluation of simulations for the development of complex decision-making skills. In: Okamoto T, Hartley R, Kinshuk, Klus JP (eds) Proceedings of the IEEE international conference on advanced learning technology: issues, achievements and challenge. IEEE computer society, Washington, DC, pp 145–148

    Chapter  Google Scholar 

  • Hitchcock D, Verheij B (2005) The Toulmin model today: introduction to the special issue on contemporary work using Stephen Edelston Toulmin’s layout of arguments. Argumentation 19:255–258

    Article  Google Scholar 

  • Holmström-Hintikka G (1995) Expert witnesses in legal argumentation. Argumentation 9(3):489–502

    Article  Google Scholar 

  • Holmström-Hintikka G (2001) Expert witnesses in the model of interrogation. In: Martino AA, Nissan E (eds) Software, Formal models, and artificial intelligence for legal evidence, special issue of Computing and Informatics, 20(6):555–579

  • Horty JF (1993) Deontic logic as founded on nonmonotonic logic. In: Meyer JJ, Wieringa R (eds) Deontic logic in computer science. Annals of Mathematics and Artificial Intelligence, vol 9. Baltzer, Basel, pp 69–91

    Google Scholar 

  • Jain AK, Bolleand R, Pankanti S (1999) Biometrics: personal identification in networked society. Kluwer (now Springer), Dordrecht

    Book  Google Scholar 

  • Jain AK, Prabhakar S, Hong L, Pankanti S (2000) Filterbank-based fingerprint matching. IEEE Transactions on Image Processing 9(5):846–859

    Article  Google Scholar 

  • Jedrzejek C, Falkowski M, Smolenski M (2009) Link analysis of fuel laundering scams and implications of results for scheme understanding and prosecutor strategy. In: Governatori G (ed) Proceedings of Legal Knowledge and Information Systems: JURIX 2009, 25 July 2009. IOS Press, Amsterdam, pp 79–88

    Google Scholar 

  • Johnson MK, Farid H (2007) Exposing digital forgeries in complex lighting environments. IEEE Transactions on Information Forensics and Security 2(3):450–461

    Article  Google Scholar 

  • Johnston VS, Caldwell C (1997) Tracking a criminal suspect through face space with a genetic algorithm. In: Bäck T, Fogel DB, Michalewics Z (eds) Handbook of evolutionary computation. Institute of Physics Publishing and Oxford University Press, Bristol and New York

    Google Scholar 

  • Josephson JR, Josephson SG (eds) (1994) Abductive inference: computation, philosophy, technology. Cambridge University Press, Cambridge, UK

    MATH  Google Scholar 

  • Kangas LJ, Terrones KM, Keppel RD, La Moria RD (2003) Computer aided tracking and characterization of homicides and sexual assaults (CATCH). Sec. 12.6 in Mena (2003), pp 364–375

  • Kaptein H, Prakken H, Verheij B (eds) (2009) Legal evidence and proof: statistics, stories, logic. Applied legal philosophy series, Ashgate Publishing, Farnham, Surrey, UK

    Google Scholar 

  • Katz DM (2013) Quantitative legal prediction—or—How I learned to stop worrying and start preparing for the data-driven future of the legal services industry. Emory Law Journal, 62, pp 909–966. http://law.emory.edu/elj/_documents/volumes/62/4/contents/katz.pdf

  • Keppens J, Schafer B (2003a) Using the box to think outside it: creative skepticism and computer decision support in criminal investigations. In: Proceedings of the IVR 21st world congress special workshop on artificial intelligence in the law: creativity in legal problem solving. http://www.meijigakuin.ac.jp/~yoshino/documents/ivr2003/keppens–schafer.pdf

  • Keppens J, Schafer B (2003b) Assumption based peg unification for crime scenario modelling. In: Proceeding of the 2003 conference on legal knowledge and information systems; JURIX 2003: the eighteenth annual conference. IOS Press, Amsterdam. http://www.jurix.nl/pdf/j05-07.pdf

  • Keppens J, Schafer B (2004) “Murdered by persons unknown”—Speculative reasoning in law and logic. In: Gordon T (ed) Legal knowledge and information systems. Jurix 2004: the seventeenth annual conference. IOS Press, Amsterdam, 2004, pp 109–118

  • Keppens J, Schafer B (2005) Assumption based peg unification for crime scenario modelling. In: Proceeding of the 2005 conference on legal knowledge and information systems; JURIX 2005: the eighteenth annual conference (frontiers in artificial intelligence and applications, 134), IOS Press, Amsterdam, pp 49–58

  • Keppens J, Schafer B (2006) Knowledge based crime scenario modelling. Expert Systems with Applications 30(2):203–222

    Article  Google Scholar 

  • Keppens J, Zeleznikow J (2002) On the role of model-based reasoning in decision support in crime investigation. In: Proceedings of the IASTED third international conference on law and technology (LawTech2002). ACTA Press, Anaheim, CA, pp 77–83

  • Keppens J, Zeleznikow J (2003) A model based reasoning approach for generating plausible crime scene scenarios from evidence. In: Sartor G (ed). Proceedings of the ninth international conference on artificial intelligence and law (ICAIL 2003), Edinburgh, Scotland, 24–28 June 2003. ACM Press, New York, pp 51–59

  • Keppens J, Shen Q, Lee M (2005) Compositional Bayesian modelling and its application to decision support in crime investigation. In: Proceedings of the 19th international workshop on qualitative reasoning, pp 138–148

  • Khuwaja GA (2006) A multimodal biometric identification system using compressed finger images. Cybernetics and Systems 37(1):23–46

    Article  Google Scholar 

  • Kuflik T, Nissan E, Puni G (1989) Finding excuses with ALIBI: alternative plans that are deontically more defensible. In: Proceedings of the International Symposium on Communication, Meaning and Knowledge vs. Information Technology, Lisbon, September 1989. Then again in Computers and Artificial Intelligence, 10(4), 1991, pp 297–325. Then in a selection from the Lisbon conference: Lopes Alves J (ed) Information Technology and Society: Theory, Uses, Impacts. Associação Portuguesa para o Desenvolvimento das Comunicações (APDC), Lisbon, and Sociedade Portuguesa de Filosofia (SPF), Lisbon, 1992, pp 484–510

  • Leary R (2012) FLINTS, a Tool for police investigation and intelligence analysis, in Nissan (2012), Chapter 7

  • MacCrimmon M (1989) Facts, stories and the hearsay rule, in Martino (1989), vol 1, pp 461–475

  • MacCrimmon M, Tillers P (eds) (2002) The dynamics of judicial proof: computation, logic, and common sense (Studies in Fuzziness and Soft Computing, 94). Physica-Verlag, Heidelberg

    Google Scholar 

  • Mani I (2001) Automatic summarization (natural language processing, 3). Benjamins, Amsterdam

    Book  Google Scholar 

  • Marks P (2014) Hands off: an app that creates maps of sexual harassment could help women in Bangladesh fight back. In: New Scientist (UK edition), vol 222(2971), 31 May 2014, p 21

  • Martino AA (ed) (1989) Pre-proceedings of the third international conference on “Logica, Informatica, Diritto: Legal expert systems”, Florence, 1989 (2 vols + Appendix). Istituto per la Documentazione Giuridica, Consiglio Nazionale delle Ricerche, Florence

  • Martino AA (1997) Quale logica per la politica. In: Martino AA (ed) In: Logica delle norme, Pisa, Italy: SEU, Servizio Editoriale Universitario di Pisa, on behalf of the Università degli Studi di Pisa, Facoltà di Scienze Politiche, pp 5–21. English translation: ‘A logic for politics’, itself accessible online at a website of Martino’s publications: http://www.antonioanselmomartino.it/index.php?option=com_content&task=view&id=26&Itemid=64

  • Martino AA, Nissan E (eds) (1998) Formal Models of Legal Time, special issue of Information and Communications Technology Law, 7(3)

  • Martino AA, Nissan E (eds) (2001) Formal approaches to legal evidence, special issue of Artificial Intelligence and Law, 9(2/3), pp 85–224

  • McGinnis JO, Pearce RG (2014) The great disruption: how machine intelligence will transform the role of lawyers in the delivery of legal services. Fordham Law Review 82:3041–3066

    Google Scholar 

  • Mena J (2003) Investigative data mining for security and criminal detection. Butterworth, Boston, MA

    Google Scholar 

  • Moens M-F (2000) Automatic indexing and abstracting of document texts. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Moens M-F (2001) Legal text retrieval. Artificial Intelligence and Law 9(1):29–57

    Article  Google Scholar 

  • Moens M-F, Uyttendaele C, Dumortier J (1997). Abstracting of legal cases: The SALOMON experience. In: Proceedings of the sixth international conference on artificial intelligence and law, Melbourne, Australia. ACM Press, New York, pp 114–122

  • Moens M-F, Uyttendaele C, Dumortier J (1999) Abstracting of legal cases: the potential of clustering based on the selection of representative objects. Journal of the American Society for Information Science 50(2):151–161

    Article  Google Scholar 

  • Murbach R, Nonn E (1991) Sentencing by artificial intelligence tools: some possibilities and limitations. In: The joint meeting of the law and society association and the research committee of the sociology of law of the International Sociological Association, Amsterdam

  • Murphy BL, Morrison RD (eds) (2002) Introduction to environmental forensics. Academic Press, San Diego, CA

    Google Scholar 

  • Nance DA, Morris SB (2002) An empirical assessment of presentation formats for trace evidence with a relatively large and quantifiable random match probability. Jurimetrics Journal 42:403–445

    Google Scholar 

  • Nirenburg S, Raskin V (2004) Principles of ontological semantics. MIT Press, Cambridge, MA

    Google Scholar 

  • Nissan E (2001a) An AI formalism for competing claims of identification: capturing the “Smemorato di Collegno” amnesia case. Computing and Informatics 20(6):625–656

    MathSciNet  MATH  Google Scholar 

  • Nissan E (2001b) ‘The Bayesianism debate in legal scholarship’ [review article on Allen and Redmayne (1997)]. Artificial Intelligence and Law 9(2/3):199–214

    Article  Google Scholar 

  • Nissan E (2012) Legal evidence, police investigation, case argumentation, and computer tools, (Law, Governance and Technology series, 5), vol 2. Springer, Dordrecht

    Book  Google Scholar 

  • Nissan E, Martino AA (eds) (2001), Software, formal models, and artificial intelligence for legal evidence, special issue of Computing and Informatics, 20(6), pp 509–656

  • Nissan E, Martino AA (eds) (2003) Building blocks for an artificial intelligence framework in the field of legal evidence, special issue (two parts) of Cybernetics and Systems, 34(4/5), pp 233–411; 34(6/7), pp 413–583

  • Nissan E, Martino AA (eds) (2004) The construction of judicial proof: a challenge for artificial intelligence modelling, special issue of Applied Artificial Intelligence, 18(3/4), pp 183–393

  • Nissan E, Hall D, Lobina E, de la Motte R (2004) A formalism for a case study in the watertime project: the city water system in Grenoble, from privatization to remunicipalization. Applied Artificial Intelligence 18(3/4):305–366

    Article  Google Scholar 

  • Nute D (ed) (1996) Defeasible deontic logic (Synthese Library, 263). Kluwer, Dordrecht

    Google Scholar 

  • Oatley G, Ewart B (2003) Crimes analysis software: “Pins in Maps”, clustering and Bayes net prediction. Expert Systems with Applications 25(4):569–588

    Article  Google Scholar 

  • Oatley G, Ewart B (2011) Data mining and crime analysis. Wiley Interdisciplinary Reviews (WIREs): Data Minining and Knowledge Discovery 1(2):147–153

    Google Scholar 

  • Oatley G, Ewart B, Zeleznikow J (2006) Decision support systems for police: lessons from the application of data mining techniques to “soft” forensic evidence. Journal of Artificial Intelligence and Law 14(1/2):35–100

    Google Scholar 

  • Pamula VK (2003) Detection of explosives, Chapter 23. In: Pearce TC, Schiffman SS, Nagle HT, Gardner JW (eds) Handbook of Machine olfaction: electronic nose technology. Wiley–VCH, Weinheim, Baden-Württemberg, Germany, pp 547–560. doi:10.1002/3527601597.ch23

    Google Scholar 

  • Pandit S, Chau DH, Wang S, Faloutsos C (2007) NetProbe: a fast and scalable system for fraud detection in online auction networks. In: WWW 2007: Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada, Track: Data Mining, Session: Mining in Social Networks. ACM, New York, pp 201–210

  • Peterson DM, Barnden JA, Nissan E (eds) (2001) Artificial intelligence and law, special issue of Information and Communications Technology Law, 10(1)

  • Poole P (2002) Logical argumentation, abduction and Bayesian decision theory: a Bayesian approach to logical arguments and its application to legal evidential reasoning, in MacCrimmon and Tillers (2002), pp 385–396

  • Prakken H (2001) Modelling reasoning about evidence in legal procedure.In: Proceedings of the eighth international conference on artificial intelligence and law (ICAIL 2001), St. Louis, Missouri. ACM Press, New York, pp 119–128

  • Prakken H (2002) Incomplete arguments in legal discourse: a case study. In: Bench-Capon TJM, Daskalopulu A, Winkels R (eds), Legal Knowledge and Information Systems. JURIX 2002: the fifteenth annual conference. IOS Press, Amsterdam, pp 93–102

  • Prakken H (2004) Analysing reasoning about evidence with formal models of argumentation. Law, Probability & Risk 3:33–50

    Article  Google Scholar 

  • Prakken H (2006) Formal systems for persuasion dialogue. The Knowledge Engineering Review 21:163–188

    Article  Google Scholar 

  • Prakken H (2008) A formal model of adjudication dialogues. Artificial Intelligence and Law 16:305–328

    Article  Google Scholar 

  • Prakken H, Renooij S (2001) Reconstructing causal reasoning about evidence: a case study. In: Verheij B, Lodder AR, Loui RP, Muntjwerff AJ (eds) Legal knowledge and information systems. Jurix 2001: the 14th annual conference, IOS Press, Amsterdam, pp 131–137

  • Prakken H, Sartor G (2002) The role of logic in computational models of logic argument: a critical survey. In: Kakas A, Sadri F (eds) Computational logic: logic programming and beyond. Essays in honour of Robert A. Kowalski, Part II (lecture notes in computer science, 2048). Springer, Berlin, pp 342–380

    Google Scholar 

  • Prakken H, Reed C, Walton DN (2003) Argumentation schemes and generalisations in reasoning about evidence. In: Sartor G (ed) Proceedings of the ninth international conference on artificial intelligence and law (ICAIL 2003), Edinburgh, Scotland, 24–28 June 2003. ACM Press, New York, pp 32–41

  • Rattani A, Mehrotra H, And H, Gupta P (2008) Multimodal biometric systems. In: Quigley M (ed) Encyclopedia of information ethics and security. IGI Global, Hershey, PA, pp 478–485

    Google Scholar 

  • Redmond MA, Blackburn C (2003) Empirical analysis of case-based reasoning and other prediction methods in a social science domain: repeat criminal victimization. In: Ashley KD, Bridge DG (eds) Case-based reasoning research and development: proceedings of the 5th international conference on case-based reasoning (ICCBR 2003), Trondheim, Norway, 23–26 June 2003, (Lecture Notes in Computer Science, 2689), Springer, Berlin

  • Reed CA, Rowe GWA (2001) Araucaria: software for puzzles in argument diagramming and XML technical report, Department of Applied Computing, University of Dundee, Dundee, Scotland. (The Araucaria software is in the public domain, and can be downloaded for free at http://www.computing.dundee.ac.uk/staff/creed/araucaria/)

  • Reed CA, Rowe GWA (2004) Araucaria: software for argument analysis, diagramming and representation. International Journal on Artificial Intelligence Tools 14(3/4):961–980

    Article  Google Scholar 

  • Rissland EL, Skalak DB (1991) CABARET: statutory interpretation in a hybrid architecture. International Journal of Man-Machine Studies 34:839–887

    Article  Google Scholar 

  • Ross A, Jain AK (2003) Information fusion in biometrics. Pattern Recognition Letters 24(13):2115–2125

    Article  Google Scholar 

  • Ruger TW, Kim PT, Martin AD, Quinn KM (2004) The Supreme Court forecasting project: legal and political science approaches to predicting Supreme Court decisionmaking. Columbia Law Review 104:1150–1209

    Article  Google Scholar 

  • Rutkin A (2014) Information from the inside: a device that keeps tabs on inmates vital signs could save lives in the slammer. New Scientist (UK edition), vol 222(2971), 31 May 2014, p 22

  • Schank RG (1972) Conceptual dependency: a theory of natural language understanding. Cognitive Psychology 3:552–631

    Article  Google Scholar 

  • Schank P, Ranney M (1995) Improved reasoning with convince me. In: CHI 95: conference companion on human factors in computing systems, ACM Press, New York, pp 276–277

  • Schank RG, Riesbeck CK (eds) (1981) Inside Computer Understanding: Five Programs Plus Miniatures. Erlbaum, Hillsdale, NJ (afterwards, Mahwa, NJ)

  • Schroeder J, Xu J, Chen H, Chau M (2007) Automated criminal link analysis based on domain knowledge. Journal of the American Society for Information Science and Technology 58(6):842–855

    Article  Google Scholar 

  • Shen Q, Keppens J, Aitken C, Schafer B, Lee M (2006) A scenario-driven decision support system for serious crime investigation. Law, Probability & Risk 5:87–117

    Article  Google Scholar 

  • Shimony SE, Nissan E (2001) Kappa calculus and evidential strength: a note on Åqvist’s logical theory of legal evidence. Artificial Intelligence and Law 9(2/3):153–163

    Article  Google Scholar 

  • Simon E, Gaes G, Rhodes W (1991) ASSYST: the design and implementation of computer assisted sentencing. Federal Probation 55:46–55

    Google Scholar 

  • Snow P, Belis M (2002) Structured deliberation for dynamic uncertain inference, in MacCrimmon and Tillers (2002), pp 397–416

  • Stranieri A, Zeleznikow J (2005) Knowledge discovery from legal databases (Springer law and philosophy library, 69). Springer, Dordrecht

    Book  Google Scholar 

  • Stranieri A, Zeleznikow J, Yearwood J (2012) Argumentation for dialectical situations, versus for structuring knowledge non-dialectically, and an integration of the two, in Nissan (2012), Sec. 3.11

  • Stranieri A, Zeleznikow J, Nissan E (2012) Using genetic algorithms in data mining, in Nissan (2012), Sec. 6.1.16

  • Surden H (2014) Machine learning and law. Washington Law Review 89:87–115

    Google Scholar 

  • Tata C, Wilson JN, Hutton N (1996) Representations of knowledge and discretionary decision-making by decision-support systems: the case of judicial sentencing. Journal of Information Law & Technology, 2. http://elj.warwick.ac.uk/jilt/artifint/2tata/pr2tata.htm

  • Thagard P (1989) Explanatory coherence. Behavioural and Brain Sciences 12(3):435–467. Commentaries and riposte up to p 502

    Article  Google Scholar 

  • Thagard P (2000) Coherence in thought and action. The MIT Press, Cambridge, MA

    Google Scholar 

  • Thagard P (2004) Causal inference in legal decision making: explanatory coherence versus Bayesian networks. Applied Artificial Intelligence 18(3/4):231–249

    Article  Google Scholar 

  • Toulmin SE (1958) The uses of argument. Cambridge University Press, Cambridge, UK (reprints: 1974, 1999)

  • Uyttendaele C, Moens M-F, Dumortier J (1998) SALOMON: automatic abstracting of legal cases for effective access to court decisions. Artificial Intelligence and Law 6(1):59–79

    Article  Google Scholar 

  • Valcour L (1997) Investigate B&E: break and enter expert system. Technical report TR1197, Canadian Police Research Centre

  • Valente A (1995) Legal knowledge engineering: a modeling approach. IOS Press, Amsterdam

    MATH  Google Scholar 

  • van den Braak SW, Vreeswijk GAW(2006) A knowledge representation architecture for the construction of stories based on interpretation and evidence, at the sixth international workshop on computational models of natural argument, held with ECAI’06, Riva del Garda, Italy, August 2006

  • van den Braak SW, van Oostendorp H, Prakken H, Vreeswijk GAW (2006) A critical review of argument visualization tools: do users become better reasoners?’, ibid

  • van Gelder TJ (2002) Argument mapping with Reason!Able [sic]. The American Philosophical Association Newsletter on Philosophy and Computers 2002:85–90

    Google Scholar 

  • Verheij B (2003) Dialectical argumentation with argumentation schemes: an approach to legal logic. Artificial Intelligence and Law 11:167–195

    Article  Google Scholar 

  • Walton D, Reed C, Macagno F (2008) Argumentation schemes. Cambridge University Press, Cambridge, UK

    Book  MATH  Google Scholar 

  • Wilson AD, Baietto M (2009) Applications and advances in electronic-nose technologies. Sensors, 9(7), 5099–5148. http://www.mdpi.com/1424-8220/9/7/5099/pdf

  • Xiang Y, Chau M, Atabakhsh H, Chen H (2005) Visualizing criminal relationships: comparisons of a hyperbolic tree and a hierachical list. Decision Support System 41:69–83

    Article  Google Scholar 

  • Xu JJ, Chen H (2004) Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks. Decision Support System 38:473–487

    Article  Google Scholar 

  • Yinon J (2003) Detection of explosives by electronic noses. Analytical Chemistry 75(5):99A–105A

    Article  Google Scholar 

  • Zeide JS, Liebowitz J (1987) Using expert systems: the legal perspective. IEEE Expert, Spring, pp 19–20

  • Zeleznikow J (2002) Using Web-based legal decision support systems to improve access to justice. Information and Communications Technology Law 11(1):15–33

    Article  Google Scholar 

  • Zeleznikow J, Stranieri A (1995) The split up system: integrating neural networks and rule based reasoning in the legal domain. In: Proceedings of the fifth international conference on artificial intelligence and law (ICAIL’95). ACM Press, New York, pp 185–194

  • Zeleznikow J, Stranieri A (1998) Split up: the use of an argument based knowledge representation to meet expectations of different users for discretionary decision making. In: Proceedings of IAAI’98: tenth annual conference on innovative applications of artificial intelligence. AAAI/MIT Press, Cambridge, MA, pp 1146–1151

Download references

Acknowledgments

I am very grateful to an anonymous referee for his or her helpful remarks. In a sense, the conclusions section is as much mine as it is that referee’s. Any remaining flaw is under my own responsibility.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ephraim Nissan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nissan, E. Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement. AI & Soc 32, 441–464 (2017). https://doi.org/10.1007/s00146-015-0596-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-015-0596-5

Keywords

Navigation