Should the criminal justice system be governed by algorithms?

In our daily life, we are frequently subject to predictive algorithms that determine music recommendations, product advertising, university admission and job placement. Until recently, I only knew such algorithms were used for those purposes. But in the American criminal justice system, predictive algorithms have been used to predict where crimes will most likely occur, who is most likely to commit a violent crime, to fail to appear at their court hearing, and who is likely to re-offend at some point in the future. When I heard about it, I was quite surprised since I am French and such tools don’t exist in our judicial system. I didn’t see the purpose of those predictive algorithms and risk assessment tools used by US Police departments and judges. How could machines be “better”, more efficient than human, since they are made by flawed people?

A few years ago, Los Angeles Police Department adopted a predictive-policing system called Predpol. Its goal was to better direct police efforts to reduce crime. It raised questions about reducing criminal activities in one place, only to have them occur elsewhere. If we think about systems predicting court appearance, their use does not always result in incarceration. They can be seen as a tool for court to bring defendants to them. However, it raises concerns about how judges use such algorithms.

What would you do if you were a United States judge who has to decide bail for a black man, a first-time offender, accused of a non-violent crime. An algorithm just told you there is a 100 percent chance he'll re-offend.Would you rely on the answer provided by the algorithm ? Is the algorithm answer more reliable than your opinion? One could argue that the answer provided by the algorithm is based on 137 question-quiz. The questions are part of a software program called Correctional Offender Management Profiling for Alternative Sanctions: COMPAS. The problem when we rely on such algorithms is their opacity

Concerns have been raised about the nature of the inputs and how they are weighted by the algorithm. We all know that human decisions are not all the times the good ones, perfect. They depend on the person deciding but at least he is individualizing each case, each person. Whereas an algorithm is incapable of doing such a thing.

Human decision-making in criminal justice settings is often flawed, and stereotypical arguments and prohibited criteria, such as race, sexual preference or ethnic origin, often creep into judgments. The question is can algorithms help prevent disproportionate and often arbitrary decisions? We can argue that no bias is desirable, and a computer can offer such an unbiased choice of architecture. But algorithms are fed with data that is not clean of social, cultural and economic circumstances.

In analyzing language we can see that natural language necessarily contains human biases. The training of machines on language entails that artificial intelligence will inevitably absorb the existing biases in a given society. Furthermore, judges already carry out risk assessments on a daily basis, for example when deciding on the probability of recidivism. This process always subsumes human experience, culture, and even biases. Human empathy and other personal qualities are in fact types of bias that overreach statistically measurable equality.

Should not such personal traits and skills or emotional intelligence be actively supported in judicial settings? I think human traits are essential in the justice process. Non-bias goal is not possible: whether because of humans or algorithms. What is important is the idea of fairness, what COMPASS system proved to failed since its predictions were racially biased.

Algorithms are mostly used in two ways: to estimate a defendant’s flight risk, and to assess his or her threat to public safety. We can disprove their utility because algorithms are biased.

One of the improvements to try to fix algorithms is through an administrative solution which includes regulatory oversight. It has been implemented in 2018, in New York with the first algorithmic accountability law in the nation. The goal of the law is fairness, accountability, and transparency. In order to do so, the law seeks to create a group of experts who will identify automated decision systems’ disproportionate impacts.

The law will allow anyone affected by an automated decision to request an explanation for the decision and will require a path for redress for anyone harmed by a decision. However, there is an issue with this law since no compliance with it is required if it would result in the disclosure of proprietary information.

One of the biggest problems with algorithms is that they are based on math, not justice. If we want an algorithm to predict something, we have to represent concepts in terms of numbers for it to process information. Issues arise from the fact that concepts like fairness and justice can’t be represented in maths because they always evolve, are subject to debates and public opinion. There is no one metric to determine fairness. We can try to change the way we design algorithms by using gender-specific risk assessments for example but disparities in the treatment of different groups will always exist. It should be decided when designing algorithms, what kind of fairness is important to prioritize? What threshold of risk should be used for release, and what kind of risk makes sense to measure in this context?

Finally, the main issue remains the lack of transparency. In fact, private providers of algorithms have asserted trade secret protections in criminal cases to protect their intellectual property. As a result, defendants are denied the right to look at the data and source code that is being used to keep them incarcerated. Without having real access to the system inside algorithms, we can say what is wrong but it is harder to say how things should be changed since we don’t clearly know how those programs run.

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