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Brave New Worlds Conference: Human vs Machine assisted Decision Making - panel John Zeleznikow Sir Zelman Cowen Centre Victoria University [email protected] +61.432 154 217 Machine Learning Technology • What is machine learning? • Machine learning is that subsection of learning in which the artificial intelligence system attempts to learn automatically. • Knowledge discovery is the non trivial extraction of implicit, previously unknown and potentially useful information from data. • Data mining is a problem-solving methodology that finds a logical or mathematical description, eventually of a complex nature, of patterns and regularities in a set of data. • Previous KDD techniques focused upon use of statistics which helped confirm hypotheses. • Machine Learning can help discover new patterns. • See Stranieri, A. and Zeleznikow, J. 2005. Knowledge Discovery from Legal Databases, Springer Law and Philosophy Library, Volume 69, Dordrecht, The Netherlands How has machine learning been applied to legal decision making? – Split Up • Split-Up provides advice on property distribution following separation (see the video) • The aim of the approach used in developing Split-Up was to identify, with domain experts, relevant factors in the distribution of property under Australian family law. • The designers then wanted to assemble a data set of values on these factors from past common place cases that can be fed to machine learning programs such as neural networks (algorithms which learn the weights of each of the factors in making a decision). • Ninety-four variables were identified as relevant for a determination in consultation with experts. • The way the factors combine was not elicited from experts as rules or complex formulas. • Rather, values on the ninety-four variables were to be extracted from cases previously decided, so that a neural network could learn to mimic the way in which judges had combined variables. • The learning is not totally automated – we needed to know the structure of the domain for data mining and explanation For what sorts of legal problems can machine assisted decision-making most gainfully used? • For ones in which there is an abundance of commonplace cases - ones that do not provide any lessons by itself, but together with numerous like cases can be used to derive conclusions. Commonplace cases are to be found in the training sets of neural networks and rule induction systems. • Machine assisted decision-making should not be used in cases where the decision-maker has ample discretion. • Machine assisted decision-making should be used in the bulk of high quantity, low value (not just financial cases) that do not result in judicial decision-making. • Thus it should be a great tool for VCAT and similar agencies as well as Legal Aid and Community Legal Centres. • Except for detecting relevant cases (via text mining) Machine assisted decision-making should not be used by judges. The need for Machine Learning to support Legal Decision Making • What are the benefits of machine-assisted decision-making in both legal and non-legal contexts versus automated machine decision-making? • • • • Efficiency Speed Cost Access What can we do to reduce power imbalances between parties that provide the information used in machine-assisted decision-making? • Make systems available to everybody • Make the use of systems free or at least cheap • Train users on how to use the system The ethics of using Machine Learning to support Legal Decision Making • Should machines ever be allowed to make legal decisions? • No, only give advice. • Who should take responsibility for automated legal decision making? • The developer of the system! • What are the benefits of machine-assisted decision-making in both legal and nonlegal contexts versus automated machine decision-making? • Machine assisted decision making can provide invaluable support re locating norms and cases, but leave individuals with the highly discretionary and ethical task of making decisions. Useful references • Kannai, R., Schild, U. and Zeleznikow, J. 2007. Modeling the evolution of legal discretion – an Artificial Intelligence Approach. Ratio Juris 20(4) December: 530–558. • Stranieri, A. and Zeleznikow, J. 2005. Knowledge Discovery from Legal Databases, Springer Law and Philosophy Library, Volume 69, Dordrecht, The Netherlands • Stranieri, A., and Zeleznikow, J. 2011. Insights from jurisprudence for machine learning in law in Kulkarni, S. (ed.), Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques, IGI Global Press: 85-98. • Zeleznikow, J. 2004. The Split-Up project: Induction, context and knowledge discovery in law. Law, Probability and Risk, 3: 147-168. • Zeleznikow, J. 2017. Can Artificial Intelligence and Online Dispute Resolution enhance efficiency and effectiveness in Courts, to appear in International journal of Court Administration. Joseph Bell Centre for Forensic Statistics and Legal Reasoning at University of Edinburgh Law School • I ran Joseph Bell Centre for Forensic Statistics and Legal Reasoning for 2 ½ years • Our goal was to investigate miscarriages of justice. • Published research includes: • Bromby, M.C. and Hall, M.J.J., 2002. The development and rapid evolution of the knowledge model of ADVOKATE: An advisory system to assess the credibility of eyewitness testimony. https://pdfs.semanticscholar.org/06ba/fd4dda6a5f60d759d6e68e1bc2d9719d0ff 5.pdf last viewed 27 February 2017 • Bromby, M., Macmillan, M. and McKellar, P., 2003. A commonkads representation for a knowledge-based system to evaluate eyewitness identification. International Review of Law, Computers & Technology, 17(1), pp.99-108.