04
Aug 2017

Innovation Popcorn: Building Judge Hercules

Written by Angus Murray and Daniel Owen (The Legal Forecast)

Survive Law and The Legal Forecast have teamed up to provide law students with bite-sized, easy-to-understand explainers on the latest law-tech and legal innovation hot topics. 

Angus Murray and Daniel Owen from The Legal Forecast explain how the use of a little thing called “data analytics” can allow lawyers to embody Judge Hercules by enabling a decision-making process that combines the best of both technological and human capabilities.

 

If you’ve had the ‘pleasure’ of studying jurisprudence already, you’d probably remember the subject in a fog. For many of us, a jurisprudence lecture is one of those times in life where we merely pretend to understand, usually with a great deal of slow nodding and a furrowed brow.

However, even in jurisprudence, the law Gods sometimes give us a nice, easily digestible concept, such as that of Judge Hercules. For those that need a refresher, Judge Hercules is the imaginary and aspirational target for judicial decision-making that jurist and philosopher Ronald Dworkin conjured up to describe the perfect Judge. With all the talk of robot lawyers stealing jobs we don’t yet even have, it is a useful time to re-examine Judge Hercules to see, even if we can’t always act like him, maybe we can artificially build him - with help from a little thing called "data analytics".

 

What's data analytics?

In contemporary society we produce a lot of data, so much in fact we now call it “big data”. For example, global internet traffic has increased from an average of approximately 100 gigabytes per day in 1992 to an astronomical 26,600 gigabytes per second[1] as of 2016. There is no hard and fast rule about how much data you have to collect before you can call it big data, but one simple definition is that it is data collected in tremendous size, volume and speed.

At the same time that data production has exploded (thanks largely to you, you despicable millennial, with your smartphone, your laptop and your Netflix), the ability of our computers to process that data has also increased. For example, did you know that Google Analytics tracks how you interact with websites and your “click through rates” to assist company’s marketing campaigns? By "data analytics", we mean the various ways that companies now harness all the data they collect and interpret what it means (i.e. by looking for patterns and trends) and building predictive models on top of it (i.e. guessing what will happen next).

 

What can you do with data analytics?

Data analytics is now capable of extracting and categorising information to identify patterns and trends. From neuroradiology to chess, data is fast becoming a new commodity of tangible value.

Even though it seems like a totally random subject (and in truth, it is), the way data analytics has been used in the world of competitive chess is a good example of how we might use data analytics in law. Why? Well, both judicial decision-making (or legal problem solving in general) has a lot in common with how players in chess make decisions - first you have to analyse all the options, evaluate their viability and then make a decision based on our imperfect human ability to assess the situation. Computer programs that use past chess games and brute force calculations to analyse the advantages and disadvantages of a particular chess position have changed the way that young players learn the game.

Garry Kasparov, arguably the greatest chess player of all time, advocates for a future punctuated by the increased use of artificial intelligence.[2] Despite his prestigious title as a Grand Master, in 1997 Kasparov suffered a controversial loss against IBM’s early chess computer, Deep Blue (in fact there is a brilliant documentary on YouTube called “Kasparov and the Machine” dedicated to the topic). The defeat prompted Kasparov to spend years studying the relationship humans have with technology. As a result, Kasparov formed the belief that the integration of humans and computers has enormous benefits for complex-decision making, both in and beyond chess.

Kasparov argues that computers are undeniably better calculators and data processors whereas humans hold superior analogical thinking, pattern-recognition and executive decision-making capabilities.[3] Kasparov believes that technology provides a “steady hand” to assist us in mitigating the damage caused by the weaknesses of the human condition including fatigue, distraction and cognitive biases. At the same time he argues that there is irreplaceable value in human intuition and its potential to complement complex data analytics.

 

How can analytics help lawyers (and almost-lawyers)?

Data analysis in the legal context (‘legal analytics’) is extremely powerful. Legal analytics enables lawyers to automate processes and subsequently reduce the time and cost of manual work. Obvious examples of the Court’s growing reliance on legal analytics include:

(a)  the Federal Court’s Practice Note on Technology and the Court;[4]

(b)  recent decisions requiring parties to perform discovery with the assistance of data analytics and automated filtering[5] (eDiscovery); and

(c)   smart contracts (which, amongst other things, automatically handle settlement procedures).

However, the Court’s progressive approach to analytics prompts the question - how will law students get experience if all the basic repetitive jobs they are usually tasked with during work experience are taken over by technology?

 

Wisconsin v Loomis

The answer lies in reconciling the growing schism in the profession between those that view the law as an industry ripe for automation and those that retain the traditional view of the law as an art form.

Recently in the US, the Wisconsin Supreme Court considered the role that automated prediction should play in determining the likelihood of recidivism. In Wisconsin v Loomis[6] (‘Loomis’), Loomis was alleged to have been involved in a drive-by shooting. The Court was required to rule on whether the use of an analytics tool in initial sentencing had violated Loomis’ constitutional right to due process.[7]

At trial, an analytics tool called the “Correctional Offender Management Profiling for Alternative Sanctions”[8] (‘COMPAS’) had been utilised. The role of COMPAS is to determine whether an offender is likely to reoffend by reference to the behaviour of past offenders in similar circumstances. COMPAS operates by taking information provided by a defendant and comparing it with publicly available data to build predictive models based on historical correlations. In Loomis’ case, COMPAS indicated that, based on available metrics and data, Loomis posed a “high risk” of reoffending[9] if released.

Ultimately the Court held that COMPAS was an acceptable assistive tool and could be utilised so long as disclosure was provided beforehand. The rationale for the Court’s decision was that the COMPAS risk assessment protocol does not predict a specific likelihood that an individual offender will reoffend. COMPAS informs the Court’s decisions but it does not take their place.[10]

The Court concluded that it was not the place of COMPAS or any other analytics software to take the place of the judiciary, but rather to enrich their capacity to make a full evaluation and determination.[11]

 

Becoming Hercules

The process driven aspects of law are, and have always been, capable of disruption. However, the art of issue spotting, effective communication, sensing untold client needs, testing witnesses and the finesse of distinguishing precedents all remain uniquely human elements of the profession.

Blending the science of analytics with the art of law is a brilliant fusion that, properly integrated, will facilitate decision-making capabilities previously thought unreachable.

Who would have guessed that Dworkin’s Judge Hercules would be a cyborg?

Enjoyed this post? Visit The Legal Forecast website or sign up for the Survive Law weekly newsletter for more.

 


[1] Cisco, “The Zettabyte Era: Trends and Analysis”, (7 June 2017) Cisco, <http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/vni-hyperconnectivity-wp.html>.

[2] Elena Holodny, “One of the greatest chess players of all time, Garry Kasparov, talks about artificial intelligence and the interplay between machine learning and humans”, (24 May 2017) Business Insider, https://www.businessinsider.com/garry-kasparov-interview-2017-5#XHgxRtYDPLj238Ch.99.  

[3] Ibid.

[4] Chief Justice Allsop, “Technology and the Court Practice Note (GPN-TECH)”, (25 October 2016) Federal Court of Australia,  <http://www.fedcourt.gov.au/law-and-practice/practice-documents/practice-notes/gpn-tech>.

[5] Redpath Contract Services Pty Ltd v Anglo Coal (Grosvenor Management) Pty Ltd [2017] QSC 149.

[6] Wisconsin v Loomis, 2016 WI 68 (WI, 2016)

[7] Ibid at [7].

[8] Ibid at [4].

[9] Ibid at [16].

[10] Ibid at [74].

[11] Ibid at [71] - [73].

Join our mailing list

1