Download Machine assisted decision-making should not be used by judges

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.