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A collection of over 250 uses for artificial intelligence

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Sentencing Recommendation Systems

Introduction

The Law Enforcement industry has been a long-suffering one in so far as the subjectivity of sentencing of criminals is concerned hence people receive sentences that are not necessarily befitting the crimes they have committed. However, of late, there have been attempts to solve such problems with the help of technological advancements such as Machine Learning (ML) and Artificial Intelligence (AI). The following are technologies that are used in the development of Sentencing Recommendation Systems (SRS), which is an AI system that uses historical data to make recommendations for judges on the best sentence to give. The intended purposes of these systems are to eliminate prejudice, achieve just punishment and in the end provide justice for all.

Challenges

There are however some challenges that come with the integration of AI and ML technologies in the law enforcement industry. The first challenge is how to capture quality and diverse data that is required to train the ML models since the data collected must be a clear reflection of the population and the crimes being committed. Another concern is that new biases may be reinforced if the data used to train the ML models is itself biased due to past sentencing data. Another concern is that the black box nature of the AI systems may result to lack of transparency and trust in the recommendations made by the system. Also, there is a question of ethics who to give such a responsibility of making such decisions to a machine.

AI Solutions

There are several AI solutions which have been designed to address these problems. First of all, data collection and preprocessing techniques are conducted carefully so that the data is diverse and representative. Some of the techniques used are: balancing the data by over-sampling fewer data and under-sampling more data. In order to increase transparency, there is a growing field called explainable artificial intelligence (XAI) which aims at making the decisions of the AI understandable to humans. In order to address bias, there is fairness-aware machine learning that changes the learning algorithm in order to avoid bias in the predictions. This is where AI ethics committees are being formed to manage the ethical implications of these systems. For instance, Northpointe company has come up with an AI solution for sentencing recommendations where they developed COMPAS, an actuarial tool that helps the courts to assess the risk of a defendant’s re-offending.

Benefits

There are numerous advantages of using ML AI in Sentencing Recommendation Systems. Some of the advantages are; improved accuracy and equity in sentencing, speed up of the judicial system and ability to assess the likelihood of re-offending to facilitate the rehabilitation process. Also, it supports the judges in making better decisions by providing them suggestions based on the AI analysis and other factors that are relevant. It also has the potential of decreasing prison population through effective identification of non-dangerous offenders.

Return on Investment

The ROI of using AI in Sentencing Recommendation Systems can be very much beneficial. It can result in cost savings as it enhances the effectiveness of the judicial system, minimises the length of undue long term imprisonment and optimises the use of resources for rehabilitation. A research done by Stanford University indicated that risk assessment instruments for example COMPAS could decrease jail admissions by 40% yet the crime rates would not be on the rise. Also, the societal ROI, which is rather intangible, may be high and involve the enhancement of the fairness and equity of the justice system.

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