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Data Science Techniques for Law and Justice: Current State of Research and Open Problems

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New Trends in Databases and Information Systems (ADBIS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 767))

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Abstract

By comparing the state of research in Legal Analysis to the needs of legal agents, we extract four fundamental problems and discuss how they are covered by the current best approaches. In particular, we review the recent statistical models, relying on Machine Learning coupled to Natural Language Processing techniques, and the Abstract Argumentation applied to the legal domain before giving some new perspectives of research.

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Notes

  1. 1.

    https://fantasyscotus.lexpredict.com/.

  2. 2.

    http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=URISERV%3Al26118.

  3. 3.

    One may notice that the less a court is predictable, the closer it is from an ideal court.

  4. 4.

    That is to say also available to legal experts.

  5. 5.

    Where the measure is calculated as given by Martin and Quinn [38, 39].

  6. 6.

    On top the predictions, the authors shown the existence of different predictability between judges, implying a difference of attitude toward the law, as well as a decrease in the SCOTUS predictability during some periods or depending on the political party at the presidence.

  7. 7.

    To be precise, some previous cases and some of their specific features. Thus, this is not a legal justification for Roman Law.

  8. 8.

    e.g.: IBM Watson Services offer query services over hundred of thousands of articles indexed every day.

References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)

    Google Scholar 

  2. Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the European court of human rights: a natural language processing perspective. PeerJ Comput. Sci. 2, 93–112 (2016)

    Article  Google Scholar 

  3. Aleven, V., Ashley, K.D.: Evaluating a learning environment for case-based argumentation skills. In: Proceedings of International Conference on Artificial Intelligence and Law (ICAIL), pp. 170–179 (1997)

    Google Scholar 

  4. Amgoud, L.: A unified setting for inference and decision: an argumentation-based approach. In: Proceedings of International Conference on Uncertainty in Artificial Intelligence (UAI), pp. 26–33 (2005)

    Google Scholar 

  5. Athakravi, D., Satoh, K., Law, M., Broda, K., Russo, A.: Automated inference of rules with exception from past legal cases using ASP. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 83–96. Springer, Cham (2015). doi:10.1007/978-3-319-23264-5_8

    Chapter  Google Scholar 

  6. Baroni, P., Romano, M., Toni, F., Aurisicchio, M., Bertanza, G.: Automatic evaluation of design alternatives with quantitative argumentation. Argum. Comput. 6(1), 24–49 (2015)

    Article  MATH  Google Scholar 

  7. Barringer, H., Gabbay, D., Woods, J.: Temporal dynamics of support and attack networks: from argumentation to zoology. In: Hutter, D., Stephan, W. (eds.) Mechanizing Mathematical Reasoning. LNCS, vol. 2605, pp. 59–98. Springer, Heidelberg (2005). doi:10.1007/978-3-540-32254-2_5

    Chapter  Google Scholar 

  8. Bench-Capon, T.J.: Representation of case law as an argumentation framework. In: Proceedings of International Conference on Legal Knowledge and Information Systems (JURIX), pp. 103–112 (2002)

    Google Scholar 

  9. Bench-Capon, T.J.: Try to see it my way: modelling persuasion in legal discourse. Artif. Intell. Law 11(4), 271–287 (2003)

    Article  Google Scholar 

  10. Cayrol, C., Lagasquie-Schiex, M.C.: On the acceptability of arguments in bipolar argumentation frameworks. In: Godo, L. (ed.) ECSQARU 2005. LNCS (LNAI), vol. 3571, pp. 378–389. Springer, Heidelberg (2005). doi:10.1007/11518655_33

    Chapter  Google Scholar 

  11. Delgado, P.: Survey of casebased reasoning as applied to the legal domain (2007, unpublished)

    Google Scholar 

  12. Delobelle, J., Haret, A., Konieczny, S., Mailly, J., Rossit, J., Woltran, S.: Merging of abstract argumentation frameworks. In: Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 33–42 (2016)

    Google Scholar 

  13. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77, 321–357 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  14. Dung, P.M., Kowalski, R.A., Toni, F.: Dialectic proof procedures for assumption-based, admissible argumentation. Artif. Intell. 170(2), 114–159 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Dung, P.M., Thang, P.M.: Towards an argument-based model of legal doctrines in common law of contracts. In: Proceedings of International Conference on Computational Logic in Multi-Agent Systems (CLIMA), pp. 111–126 (2008)

    Google Scholar 

  16. Dung, P.M., Thang, P.M.: Towards (probabilistic) argumentation for jury-based dispute resolution. In: Proceedings of International Conference on Computational Models of Argument (COMMA), pp. 171–182 (2010)

    Google Scholar 

  17. Frydman, B.: Le sens des lois: histoire de l’interprétation et de la raison juridique. Penser le droit, Bruylant, Bruxelles (2005)

    Google Scholar 

  18. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  MATH  Google Scholar 

  19. Guimerà, R., Sales-Pardo, M.: Justice blocks and predictability of U.S. supreme court votes. PLOS ONE 6(11), 1–8 (2011)

    Article  Google Scholar 

  20. Hadoux, E., Beynier, A., Maudet, N., Weng, P., Hunter, A.: Optimization of probabilistic argumentation with markov decision models. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp. 2004–2010 (2015)

    Google Scholar 

  21. Hadoux, E., Beynier, A., Weng, P.: Sequential decision-making under non-stationary environments via sequential change-point detection. In: Workshop on Learning over Multiple Contexts (LMCE) at ECML-PKDD (2014)

    Google Scholar 

  22. Hambleton, R.: Fundamentals of Item Response Theory. Measurement Methods for the Social Science. SAGE Publications, Newbury Park (1991)

    Google Scholar 

  23. Islam, M.R., Hossain, K., Krishnan, S., Ramakrishnan, N.: Inferring multi-dimensional ideal points for US supreme court justices. In: Proceedings of International Conference on Artificial Intelligence (AAAI), pp. 4–12 (2016)

    Google Scholar 

  24. Jøsang, A.: Subjective Logic: A Formalism for Reasoning Under Uncertainty. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Switzerland (2016)

    Book  MATH  Google Scholar 

  25. Kannai, R., Schild, U.J., Zeleznikow, J.: There is more to legal reasoning with analogies than case based reasoning, but what? In: Dershowitz, N., Nissan, E. (eds.) Language, Culture, Computation. Computing of the Humanities, Law, and Narratives. LNCS, vol. 8002, pp. 440–451. Springer, Heidelberg (2014). doi:10.1007/978-3-642-45324-3_15

    Chapter  Google Scholar 

  26. Katz, D.M., Bommarito, M.J.: Fin(legal)tech - law’s future from finance’s past (talk) (2017). https://speakerdeck.com/danielkatz/fin-legal-tech-laws-future-from-finances-past-professors-daniel-martin-katz-plus-michael-j-bommarito. Accessed 21 Mar 2017

  27. Katz, D.M., Bommarito, M.J., Blackman, J.: Predicting the behavior of the supreme court of the united states: a general approach. SSRN Electron. J. (2014)

    Google Scholar 

  28. Kowalski, A.: Case-based reasoning and the deep structure approach to knowledge representation. In: Proceedings of International Conference on Artificial Intelligence and Law (ICAIL), pp. 21–30 (1991)

    Google Scholar 

  29. Kowalski, R.A., Toni, F.: Abstract argumentation. Artif. Intell. Law 4(3), 275–296 (1996)

    Article  Google Scholar 

  30. Lauderdale, B.E., Clark, T.S.: Scaling politically meaningful dimensions using texts and votes. Am. J. Polit. Sci. 58(3), 754–771 (2014)

    Article  Google Scholar 

  31. Leite, J., Martins, J.: Social abstract argumentation. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp. 2287–2292 (2011)

    Google Scholar 

  32. Mailly, J.: Dynamic of argumentation frameworks. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pp. 3233–3234 (2013)

    Google Scholar 

  33. Martin, A.D., Quinn, K.M., Epstein, L.: The median justice on the united states supreme court. N.C. Law Rev. 83, 1275–1322 (2004)

    Google Scholar 

  34. Martin, A.D., Quinn, K.M., Kim, P.T., Ruger, T.W.: Competing approaches to predicting supreme court decision making. Perspect. Polit. 2, 761–767 (2004)

    Article  Google Scholar 

  35. Modgil, S., Faci, N., Meneguzzi, F., Oren, N., Miles, S., Luck, M.: A framework for monitoring agent-based normative systems. In: Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 153–160 (2009)

    Google Scholar 

  36. Nirn, O.: An Argumentation Framework Supporting Evidential Reasoning with Applications to Contract Monitoring. Ph.D. thesis, University of Aberdeen (2007)

    Google Scholar 

  37. Ontañón, S., Plaza, E.: An argumentation-based framework for deliberation in multi-agent systems. In: Rahwan, I., Parsons, S., Reed, C. (eds.) ArgMAS 2007. LNCS (LNAI), vol. 4946, pp. 178–196. Springer, Heidelberg (2008). doi:10.1007/978-3-540-78915-4_12

    Chapter  Google Scholar 

  38. Quinn, K.M., Martin, A.D.: Dynamic ideal point estimation via markov chain Monte Carlo for the U.S. supreme court, 1953–1999. Polit. Anal. 10(2), 134–153 (2002)

    Article  Google Scholar 

  39. Quinn, K.M., Park, J.H., Martin, A.D.: Improving judicial ideal point estimates with a more realistic model of opinion content (2006, unpublished)

    Google Scholar 

  40. Rago, A., Toni, F., Aurisicchio, M., Baroni, P.: Discontinuity-free decision support with quantitative argumentation debates. In: Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 63–73 (2016)

    Google Scholar 

  41. Rissland, E.L.: AI and similarity. IEEE Intell. Syst. 21(3), 39–49 (2006)

    Article  Google Scholar 

  42. Ruger, T.W., Kim, P.T., Martin, A.D., Quinn, K.M.: The supreme court forecasting project: legal and political science approaches to predicting supreme court decisionmaking. Columbia Law Rev. 104(4), 1150–1210 (2004)

    Article  Google Scholar 

  43. Segal, J.A., Cover, A.D.: Ideological values and the votes of U.S. supreme court justices. Am. Polit. Sci. Rev. 83(2), 557–565 (1989)

    Article  Google Scholar 

  44. Segal, J.A., Epstein, L., Cameron, C.M., Spaeth, H.J.: Ideological values and the votes of U.S. supreme court justices revisited. J. Polit. 57(3), 812–823 (1995)

    Article  Google Scholar 

  45. Sim, Y., Routledge, B.R., Smith, N.A.: The utility of text: the case of amicus briefs and the supreme court. Comput. Res. Repos. (2014)

    Google Scholar 

  46. Spitzer, M.L., Cohen, L.: Solving the chevron puzzle. J. Law Contemp. Probl. 57, 65–110 (1994)

    Article  Google Scholar 

  47. Sunstein, C.R.: How law constructs preferences. Georget. Law J. 86, 2637–2652 (1998)

    Google Scholar 

  48. Tolosi, L., Lengauer, T.: Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27(14), 1986–1994 (2011)

    Article  Google Scholar 

  49. Troper, M.: La théorie du droit, le droit, l’état. In: Léviathan. Presses universitaires de France (2001)

    Google Scholar 

  50. Čyras, K., Satoh, K., Toni, F.: Abstract argumentation for case-based reasoning. In: Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR), pp. 549–552 (2016)

    Google Scholar 

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Authors and Affiliations

  1. IBM Poland Software Lab, Cracow, Poland

    Alexandre Quemy

  2. Faculty of Computing, Poznań University of Technology, Poznań, Poland

    Alexandre Quemy

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  1. Alexandre Quemy
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Editors and Affiliations

  1. Riga Technical University , Riga, Latvia

    Mārīte Kirikova

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  3. University of Cyprus , Nicosia, Cyprus

    George A. Papadopoulos

  4. Free University of Bozen-Bolzano , Bozen-Bolzano, Italy

    Johann Gamper

  5. Institute of Computing Science, Poznan University of Technology, Poznan, Poland

    Robert Wrembel

  6. Université Lumière Lyon 2, Lyon, France

    Jérôme Darmont

  7. University of Bologna , Bologna, Italy

    Stefano Rizzi

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Quemy, A. (2017). Data Science Techniques for Law and Justice: Current State of Research and Open Problems. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-67162-8_30

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