A Brief History of the Changing Roles of Case Prediction in AI and Law

Kevin D. Ashley   | Bio
University of Pittsburgh
Share:

Abstract

Predicting case outcomes has long played a role in research on Artificial Intelligence and Law. Actually, it has played several roles, from identifying borderline cases worthy of legal academic commentary, to providing some evidence of the reasonableness of computational models of case-based legal reasoning, to providing the raison d'être of such models, to accounting for statistically telling features beyond such models, to circumventing features altogether in favor of predicting outcomes directly from analyzing case texts. The use cases to which case prediction has been put have also evolved. This article briefly surveys this historical evolution of roles and uses from a mere research possibility to a fundamental tool in AI and Law’s kit bag of techniques.

 

References

  1. Aletras, N., D. Tsarapatsanis, D. Preoţiuc-Pietro, and V. Lampos. 2016. “Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective.” PeerJ Computer Science, 2: e93.
  2. Aleven, V. 2003. “Using background knowledge in case-based legal reasoning: a computational model and an intelligent learning environment.” Artificial Intelligence, 150 (1–2): 183–237.
  3. Ashley, K. 2017. Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge, UK: Cambridge University Press.
  4. Ashley, K. and Brüninghaus, S. 2006. “Computer models for legal prediction.” Jurimetrics, 46 (3): 309–52.
  5. Ashley, K. and Brüninghaus, S. 2009. “Automatically classifying case texts and predicting outcomes.” Artificial Intelligence and Law, 17 (2): 125–65.
  6. Branting, K. et al. 2019. “Semi-Supervised Methods for Explainable Legal Prediction.” Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law, ICAIL ’19, June 17–21, Montreal, QC, CA, pp. 22-31.
  7. Branting, L. K., Yeh, A., Weiss, B., Merkhofer, E., and Brown, B. 2017, October. “Cognitive Assistance for Administrative Adjudication.” In 2017 AAAI Fall Symposium Series.
  8. Branting, L. K., Yeh, A., Weiss, B., Merkhofer, E., and Brown, B. 2018. “Inducing predictive models for decision support in administrative adjudication.” In U. Pagallo et al. (eds.) AI Approaches to the Complexity of Legal Systems. AICOL International Workshops 2015-2017, LNAI 1071, Springer, Cham, pp. 465-477.
  9. Chorley, A. and Bench-Capon, T. 2005. “AGATHA: using heuristic search to automate the construction of case law theories.” Artificial Intelligence and Law, 13 (1): 9–51.
  10. Falakmasir, M. and Ashley, K. 2017. “Utilizing Vector Space Models for Identifying Legal Factors from Text”, 30th Int’l Conf. on Legal Knowledge and Information Systems. Jurix 2017, Amsterdam: IOS Press 183.
  11. Gordon, T., Prakken, H., and Walton, D. 2007. “The Carneades model of argument and burden of proof.” Artificial Intelligence, 171 (10–5): 875–96.
  12. Grabmair, M. 2016. Modeling Purposive Legal Argumentation and Case Outcome Prediction using Argument Schemes in the Value Judgment Formalism. Ph.D. thesis, University of Pittsburgh, Pittsburgh, PA.
  13. Hutchinson, B., and Mitchell, M. 2019, January. “50 Years of Test (Un) fairness: Lessons for Machine Learning.” In Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, pp. 49-58.
  14. Katz, D., Bommarito, M. and Blackman, J. A. 2017. “General Approach for Predicting the Behavior of the Supreme Court of the United States.” PLoS ONE 12(4): e0174698. https://doi.org/10.1371/journal.pone.0174698
  15. Katz, D., Bommarito, M. II, and Blackman, J. 2014. “Predicting the Behavior of the Supreme Court of the United States: A General Approach.” ARXIV.ORG, at 6 (2014), https://arxiv.org/pdf/1407.6333.pdf [https://perma.cc/JXX5-WQBY].
  16. Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., and Mullainathan, S. 2017. “Human decisions and machine predictions.” The quarterly journal of economics, 133 (1): 237-293.
  17. Lakkaraju, H., and Rudin, C. 2016. “Learning cost-effective treatment regimes using Markov decision processes.” arXiv preprint arXiv:1610.06972.
  18. Lin, C.-Y. 2004. “Rouge: A package for automatic evaluation of summaries.” Proceedings of the ACL-04 Workshop Text Summarization Branches Out”, Barcelona, Conference held in conjunction with ACL, pp. 74–81.
  19. Mackaay, E., Robillard, P. 1974. “Predicting judicial decisions: the nearest neighbor rule and visual representation of case patterns”, Datenverarbeitung im Recht, 3: 302–31.
  20. Medvedeva, M., Vols, M., and Wieling, M. 2019. “Using machine learning to predict decisions of the European Court of Human Rights.” Artificial Intelligence and Law, pp. 1-30. https://doi.org/10.1007/s10506-019-09255-y
  21. Surdeanu, M., Nallapati, R., Gregory, G., Walker, J., and Manning, C. 2011. “Risk analysis for intellectual property litigation.” Proceedings of the 13th International Conference on Artificial Intelligence and Law. New York, NY: ACM, pp. 116–20.
  22. Susskind, R. 2010. The End of Lawyers? Rethinking the Nature of Legal Services. Oxford: Oxford University Press.
  23. Zeleznikow, J. and A. Stranieri. 1995. "The Split-Up System: Integrating Neural Networks and Rule-Based Reasoning in the Legal Domain." In Proceedings ICAIL-95. New York: ACM, pp. 185-194.
  24. Zhong, L., Z. Zhong, Z. Zhao, S. Wang, K. Ashley, and Grabmair, M. 2019. “Automatic Summarization of Legal Decisions Using Iterative Masking of Predictive Sentences.” In Proceedings ICAIL-19, New York: ACM, pp. 163-172.
How to Cite
1.
Ashley KD. A Brief History of the Changing Roles of Case Prediction in AI and Law. LiC [Internet]. 2019Aug.15 [cited 2020Jul.5];36(1):93-112. Available from: https://journals.latrobe.edu.au/index.php/law-in-context/article/view/88

Send mail to Author


Send Cancel