Negating Schrodinger’s Justice through AI Transparency

September 29, 2018

William Perrin, former Cabinet Office civil
servant and transparency campaigner, coined the phrase ‘
Schrodinger’s justice’ to,
in part, describe the risks involved with adopting automated decision-making
and AI in the criminal justice system. The negative implication being that large
parts of court proceedings and decision-making will happen in a virtual black
box away from any kind of scrutiny.

While I wholeheartedly respect the sanctity
of our legal system and believe it is absolutely necessary (in such an
important area of government) to ensure that new technologies are transparent
and unbiased, I can’t help thinking that the furore surrounding Schrodinger’s justice is just another example
of sensationalism, scaremongering and the unhealthy amount of ignorance surrounding

Ignorance breeds fear and mistrust. An
example of this is The Royal Society of Arts (RSA) and YouGov’s
research earlier
this year which found that 83% of the British public are unfamiliar with the
use of automated decision-making in the criminal justice system. Despite this self-professed
lack of familiarity, 60% said they oppose its use.

July 2018 saw the first public
evidence-gathering hearing of the 
Law Society’s Technology and Law Policy Commission on ‘Algorithms
in the Justice System
’. Over the next six months the commission
aims to examine the use of algorithms in the justice system in England and
Wales and what controls, if any, are needed to protect human rights and trust
in the justice system.

I had high hopes for the hearing but was
disappointed to read that the main recommendation from the first session seemed
to be that the Government should set up an
independent public register of artificial intelligence
to ensure that automated decision-making by
the police, courts and other justice agencies is open to public scrutiny.

For me, that’s not really dealing with the

There are already a few examples of Police
Forces using algorithms.  Probably the most widely publicised is
Durham Police’s HART algorithm pilot, which was created to
assist custody officers in deciding whether or not a suspect should be
released, kept in the cell, or made eligible for a local rehabilitation
programme ‘Checkpoint’.

Durham’s algorithm
is a black box. Therefore, it isn’t possible for the system to fully explain
how it makes decisions. As a result HART has faced its fair share of negative
publicity and
accusations of discriminating
against the poor

Michael Barton,
Chief Constable, of Durham Constabulary took part in the recent ‘Algorithms in
the Justice System’ hearing to
defend HART and reassure everyone that it is
ntended as a decision support tool and would
never take the kind of nuanced decisions made by custody officers.

In the current AI climate, any black box automated
decision-making platform that makes it difficult, or impossible, to understand
exactly how a decision was reached is asking for trouble. The House of Lords
Select Committee on AI has already expressed its
view that ‘…it is unacceptable to deploy any AI system
that could have a substantial impact on an individual’s life, unless it can
generate a full and satisfactory explanation for the decisions it will take.’

Transparency has become a
technical issue that developers must get right. I’ve written extensively about how
developers can build transparency into AI systems and the issues stemming from machine
learning and statistical methods of data analysis. In designing algorithms to
learn answers, rather than have them explicitly programmed, most machine learning
techniques, by their nature, are black box.

As an example, take an image
classification task using the popular deep learning technique of Convolutional
Neural Networks (CNN). CNN relies on a large network of weighted nodes but when
it classifies an image it’s extremely difficult to understand the features
extracted to make that classification. Consequently we are clueless as to what
the nodes of a CNN actually represent.

‘Explainer algorithms’ that
can be applied alongside other statistical techniques are one way of tackling
this issue. Techniques such as Random Decision Forests (RDF) also open the door
to better understanding of feature extraction.

Ultimately I believe what we really need are more technologies clearly modelling
automated decision-making on human expertise — rather than black box data.
is designed for collaboration with people, so building human expertise into the
development of these platforms and enabling AI systems to provide clear
explanations for the decisions they make (in a format that those same human
experts understand and can confirm) is critical to the future of automated decision-making
platforms in the criminal justice system and negating the risks of Schrodinger’s justice.

Technological breakthroughs will always be
polarising because there are often both benefits and risks. But the ultimate
success of AI and automated decision-making hinges on the uphill battle to
build public trust. More explainable and transparent AI systems is the best way
to win that battle.

Ben Taylor is CEO at Rainbird and advisor
to the All-Party Parliamentary Group on Artificial Intelligence (