Menu

Search

  |   Insights & Views

Menu

  |   Insights & Views

Search

Don't fear robo-justice. Algorithms could help more people access legal advice

0
comments

Algorithms have a role to play in supporting but not replacing the role of lawyers.

Around 15 years ago, my team and I created an automated tool that helped determine eligibility for legal aid. Known as GetAid, we built it for Victoria Legal Aid (VLA), which helps people with legal problems to find representation. At that time, the task of determining who could access its services chewed up a significant amount of VLA’s operating budget.

After passing a financial test, applicants also needed to pass a merit test: would their case have a reasonable chance of being accepted by a court? GetAid provided advice about both stages using decision trees and machine learning.

It never came online for applicants. But all these years later, the idea of using tools such as GetAid in the legal system is being taken seriously. Humans now feel far more comfortable using software to assist with, and even make, decisions. There are two major reasons for this change:

  • Efficiency: the legal community has moved away from charging clients in six-minute blocks and instead has become concerned with providing economical advice.
  • Acceptance of the internet: legal professionals finally acknowledge that the internet can be a safe way of conducting transactions and can be used to provide important advice and to collect data.

This is a good development. Intelligent decision support systems can help streamline the legal system and provide useful advice to those who cannot afford professional assistance.

Intelligent legal decision support systems

While robots are unlikely to replace judges, automated tools are being developed to support legal decision making. In fact, they could help support access to justice in areas such as divorce, owners corporation disputes and small value contracts.

In cases where litigants cannot afford the assistance of lawyers or choose to appear in court unrepresented, systems have been developed that can advise about the potential outcome of their dispute. This helps them have reasonable expectations and make acceptable arguments.

Our Split-Up software, for example, helps users understand how Australian Family Court judges distribute marital property after a divorce.

The innovative part of the process is not the computer algorithm, but dividing the process into 94 arguments, including issues such as the contributions of the wife relative to the husband; the future needs of the wife relative to the husband; and the marriage’s level of wealth.

Using a form of statistical machine learning known as a neural network, it examines the strength of the weighting factors – contributions, needs and level of wealth – to determine an answer about the possible percentage split.

Other platforms follow a similar model. Developed by the Dutch Legal Aid Board, the Rechtwijzer dispute resolution platform allows people who are separating to answer questions that ultimately guide them to information relevant to their family situation.

Another major use of intelligent online dispute resolution is the British Columbia Civil Resolution System. It helps people affordably resolve small claims disputes of C$5,000 and under, as well as strata property conflicts.

Its initiators say that one of the common misconceptions about the system is that it offers a form of “robojustice” – a future where “disputes are decided by algorithm”.

Instead, they argue the Civil Resolution Tribunal is human-driven:

From the experts who share their knowledge through the Solution Explorer, to the dispute resolution professionals serving as facilitators and adjudicators, the CRT rests on human knowledge, skills and judgement.

Concerns about the use of robo-justice

Twenty years after we first began constructing intelligent legal decision support systems, the underlying algorithms are not much smarter, but developments in computer hardware mean machines can now search larger databases far quicker.

Critics are concerned that the use of machine learning in the legal system will worsen biases against minorities, or deepen the divide between those who can afford quality legal assistance and those who cannot.

There is no doubt that algorithms will continue to perform existing biases against vulnerable groups, but this is because the algorithms are largely copying and amplifying the decision-making trends embedded in the legal system.

In reality, there is already a class divide in legal access – those who can afford high quality legal professionals will always have an advantage. The development of intelligent support systems can partially redress this power imbalance by providing users with important legal advice that was previously unavailable to them.

There will always be a need for judges with advanced legal expertise to deal with situations that fall outside the norm. Artificial intelligence relies upon learning from prior experience and outcomes, and should not be used to make decisions about the facts of a case.

Ultimately, to pursue “real justice”, we need to change the law. In the meantime, robots can help with the smaller stuff.

The ConversationJohn Zeleznikow has received research funding from the Australian Research Council, Relationships Australia Queensland, Relationships Australia Victoria, Victoria Legal Aid, Software Engineering Australia, Phillips and Wilkins, Allan Moore and Company, Victorian Institute of Sport and Tennis Australia. His partner works for Relationships Australia Victoria.

  • ET PRO
  • Market Data

Market-moving news and views, 24 hours a day >

2017-11-23 16:31:32
0m
2017-11-23 16:30:58
0m

November 23 21:00 UTC Released

KRConsumer Sentiment Ind*

Actual

112.3 Bln USD

Forecast

Previous

112.3 Bln USD

November 23 19:00 UTC Released

AREconomic Activity YY*

Actual

955 %

Forecast

-800 %

Previous

1755 %

November 23 23:50 UTC 9090m

JPForeign Bond Investment

Actual

Forecast

Previous

-105 Bln JPY

November 23 23:50 UTC 9090m

JPForeign Invest JP Stock

Actual

Forecast

Previous

182.4 Bln JPY

November 24 00:30 UTC 130130m

JPNikkei Mfg PMI Flash

Actual

Forecast

Previous

52.8 bln $

November 24 09:00 UTC 640640m

DEIfo Business Climate*

Actual

Forecast

116.6 %

Previous

116.7 %

November 24 09:00 UTC 640640m

DEIfo Current Conditions*

Actual

Forecast

125 %

Previous

124.8 %

November 24 09:00 UTC 640640m

DEIfo Expectations*

Actual

Forecast

108.9 %

Previous

109.1 %

November 24 09:00 UTC 640640m

ITIndustrial Orders MM SA

Actual

Forecast

Previous

8.7 %

November 24 09:00 UTC 640640m

ITIndustrial Orders YY NSA

Actual

Forecast

Previous

12.2 %

Close

Welcome to EconoTimes

Sign up for daily updates for the most important
stories unfolding in the global economy.