Henry Prakken, Professor of AI & Law, Department of Information and Computing Sciences, Utrecht University.

Lecture Series Topic Topic

Driven by the spectacular progress in AI in recent years, especially in machine learning, natural-language processing and generative AI, there has been increasing attention for the question how artificial intelligence (AI) can support judicial decision-making. Since 2016, algorithmic case outcome predictors have received much attention. Some hope that supporting judges with such algorithms can increase the efficiency, predictability and consistency of judicial decision-making. It has even been suggested that such algorithms can be used to automate decision-making in routine cases so that judges have more time for complex cases.

Others argue that the claimed benefits of such algorithms are based on misunderstandings concerning their nature and that for supporting or automating judicial decision-making a very different kind of AI system is needed, namely, knowledge-based algorithms that can apply legal knowledge to justify legal decisions. A more general concern with AI support for judges, whether data-driven or knowledge-based, is that this would force mechanical application of the law without room for individual justice and for creative interpretation of the law.

More recently, since the introduction of ChatGPT in November 2022 there is much research on applications of generative AI to legal reasoning. Never before has an AI tool been available to so many and so easy to use for so many different tasks. The ease with which ChatGPT generates fluent and linguistically flawless texts of many types and in many areas is astonishing. Many legal applications of large language models (LLM, the technology underlying ChatGPT) are currently being developed, studied and applied in practice.

However, their ease of use also creates a danger, since it makes many people blindly trust that what ChatGPT or an LLM says says is true. This trust is often unfounded, because an LLM essentially does nothing more than predict the most probable next word in a sequence of words. As a consequence, such models often 'hallucinate' evidently untrue 'facts'. Moreover, their behaviour can be inconsistent in that they often give different answers when the same question is asked multiple times.

The aim of this series of lectures is to discuss these developments and issues from the point of view of AI & law but also from a legal-theoretical and philosophical perspective.

Programme

Lecture 1 (Nov. 19th, 10.00-12.00): Rule-Based AI & Law Models of Legal Argument Reading: L.K. Branting, Data-centric and logic-based approaches for automated problem solving. Artificial Intelligence and Law 25 (2017): 5-27. H. Prakken, Logical models of legal argumentation. In M. Knauff & W. Spohn (eds.): The Handbook of Rationality, pp. 669-677. Cambridge, MA: MIT Press 2021. H. Prakken, Logics of argumentation and the law. In H.P. Glenn & L.D. Smith (eds): Law and the New Logics. Cambridge University Press 2017, pp. 3-31. H. Prakken & G. Sartor, Law and logic: a review from an argumentation perspective. Artificial Intelligence 227 (2015): 214-245. Online reader Computational Argumentation, Henry Prakken, August 2024 (Ch. 4+5 are on Dung, Ch. 6 is on ASPIC+). Powerpoint slides (see also the notes). Lecture 2 (Nov. 20st, 10.00-12.00): AI & Law Models of Case-Based Reasoning Reading: J. Horty, Rules and reasons in the theory of precedent. Legal Theory 17 (2011), 1-33. Chapter 10 of Henry Prakken, Computational Argumentation. Reader for a master course at Utrecht University, August 2024. Section 3.3 of H. Prakken & G. Sartor, Law and logic: a review from an argumentation perspective. Artificial Intelligence 227 (2015): 214-245. T.J.M. Bench-Capon, HYPO's legacy: Introduction to the virtual special issue. Artificial Intelligence and Law 25 (2017): 205-250. H. Prakken, A formal analysis of some factor- and precedent-based accounts of precedential constraint. Artificial Intelligence and Law 29 (2021): 559-585. [PDF], [Publisher's link] J. Nouwens, A.M. Borg & H. Prakken, An application of case-based reasoning to decision-making in Dutch administrative law. In J. Savelka et al. (eds.), Legal Knowledge and Information Systems. JURIX 2024: The Thirty-Seventh Annual Conference, to appear. Amsterdam etc, IOS Press (2024). [PDF]. J. Peters, F.J. Bex & H. Prakken, Model- and data-agnostic justifications with a fortiori case-based argumentation. Proceedings of the 19th International Conference on Artificial Intelligence and Law, Braga (Portugal) 2023. New York: ACM Press 2023, 207-216. [PDF] W. van Woerkom, D. Grossi, H. Prakken & B. Verheij, A Case-based-reasoning analysis of the COMPAS dataset. In J. Savelka et al. (eds.), Legal Knowledge and Information Systems. JURIX 2024: The Thirty-Seventh Annual Conference, to appear. Amsterdam etc, IOS Press (2024). [PDF]. Powerpoint slides (see also the notes). Lecture 3 (Nov. 25th, 14.00-16.00): Generative AI & law Reading: H. Prakken, Can computers argue like a lawyer?. Inaugural address Utrecht University, September 9, 2024. H. Prakken, On evaluating legal-reasoning capabilities of generative AI. In Proceedings of the 24th Workshop on Computational Models of Natural Argument, 100-112. CEUR-WS Vol. 3769. [PDF]. S. Kapoor, P. Henderson & A. Narayanan, Promises and pitfalls of artificial intelligence for legal applications. Journal of Cross-Disciplinary Research in Computational Law 2024, Vol 2, no 2. [PDF]. Powerpoint slides (see also the notes). PDF slides (without notes). Lecture 4 (Nov. 26th, 15.00-17.00): Legal Judgement Prediction Reading: M. Medvedeva & P. McBride, Legal judgment prediction: if you are going to do it, do it right. Proceedings of the Natural Legal Language Processing Workshop 2023, pp. 73-84. Association for Computational Linguistics, 2023. F.J. Bex & H. Prakken, On the relevance of algorithmic decision predictors for judicial decision making. Proceedings of the 18th International Conference on Artificial Intelligence and Law, Sao Paolo (Brazil) 2021. New York: ACM Press 2021, 175-179. F.J. Bex & H. Prakken, Can predictive justice improve the predictability and consistency of judicial decision-making? In E. Schweighofer (ed.), Legal Knowledge and Information Systems. JURIX 2021: The Thirty-Fourth Annual Conference. Amsterdam etc, IOS Press (2021), 207-214. K.D. Ashley, A brief history of the changing roles of case prediction in AI and law. Law in Context. A Socio-Legal Journal 36, 1 (2019), 93-112. F.J. Bex and H. Prakken, The legal prediction industry: meaningsless hype or useful development? English translation of an article published in Ars Aequi 69 (2020), 255-259. F. Pasquale & G. Cashwell, Prediction, persuasion, and the jurisprudence of behaviourism. University of Toronto Law Journal, Vol 68, Supplement 1, 2018, pp. 63-81. H. Prakken, Is the robot judge near? English translation of an article published in Nederlands Juristenblad 2018/207, no. 4, pp. 269-274. Powerpoint slides (see also the notes). Lecture 5 (Nov. 28th, 15.00-17.00): Bayesian Reasoning in Court Reading: H. Prakken, Recognising and avoiding fallacies in interpreting statistical evidence. To appear in S. Brewer, J. Maranhao & G. Sartor (eds), Logic and Law. Edward Elgar Publishing. H. Prakken, F.J. Bex & A.R. Mackor, Editors' Review and Introduction: Models of Rational Proof in Criminal Law. Topics in Cognitive Science 12:4 (2020), 1053-1067. [PDF] Powerpoint slides (see also the notes).