AI and Ethics: Criminal Justice
By: Aaryan Mahipal
Tags: Machine Learning, Artificial Intelligence, Inspirit Ai, Criminal Justice, Ethics

While criminal justice risk assessment tools are often simpler than the deep neural networks used in many modern artificial intelligence systems, they are basic forms of AI full of bias. As such, they present a paradigmatic example of the high-stakes social and ethical consequences of automated AI decision-making.

There is software used across the United States to predict future criminals. Risk assessment scores are increasingly common in courtrooms across the nation. They are used to inform decisions about who can be set free at every stage of the criminal justice system, from assigning bond amounts to even more fundamental decisions about defendant’s freedom.

However, in forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.

The algorithm was particularly likely to falsely flag black defendants as future criminals, wrongly labelling them this way at almost twice the rate as white defendants. White defendants were mislabeled as low risk more often than black defendants.

That being said, this disparity could not be explained by the statistical data that was used to train the machine learning algorithm. Neither the training data nor the testing data had racial bias inflicted in it. Already, the present-day justice system has made the minority population an easy target to portray the idealistic justice numbers and statistical data that we are now accustomed to, even more, has now creeped into artificial intelligence.

The model I studied was COMPAS (Correctional Offender Management Profiling for Alternative Sanctions). It is an algorithmic risk-assessment tool that attempts to predict whether a defendant is likely to recidivate.

Even though the dataset did consider the race and ethnicity of the defendants, there was no inherent bias in the dataset yet in the proposed output.

In order to eliminate this bias, I first had to understand — What do we mean by ‘fair’?

To me, fairness in the justice system means equal standards applied to all, independent of any personal or identifying characteristics. It implies speedy and thorough due process for all people and presumption of innocence until proven guilty.

In order to achieve this, few preliminary steps were taken during the data and ML setup:

Categories which were not deemed important were eliminated such as race, color, ethnicity, etc

All groups were predicted to have equal probability of recidivism in the beginning.

Given below is an example of a few rows of the indexed dataset:

The question remains, How did I make the data fair?

It was integral to make the mean or average of two categories equal. The formula for group fairness that I implemented is given below:

P(y_hat = 1 | a = y_hat) = P(y_hat = 1 | R = c)

After this, the mean and standard deviation were set to be the same.

This is how I, if not eliminated, reduced the bias in the COMPAS model.

However, let’s come back to a more general purview. In my opinion, we as a society have allowed our stereotypes to be testaments to certain decisions which on the most basic level is unethical. Social networks were once created with the intention of having a tool. It now drives the world, right from emotional manipulation, buyer influence and voting polarization.

Therefore, I’d like to think of AI in criminal justice more of a ‘tool’ rather than something that does certain tasks more efficiently than humans, assumably. This would achieve two important goals:

It would prevent unemployment in the near future in the Criminal Justice Sector

It would suggest instead of dictate. This would help us avoid unfair sentencing even if there is a racial bias in the suggestion.

Aaryan Mahipal is a Student Ambassador in the Inspirit AI Student Ambassadors Program. Inspirit AI is a pre-collegiate enrichment program that exposes curious high school students globally to AI through live online classes. Learn more at https://www.inspiritai.com/.