Source: Moritz Law OSU

Moritz Law OSU publishes a blog post by Katelyn Oswalt examining the increasing role of Artificial Intelligence in criminal sentencing and risk assessment. The article explores how courts are utilizing AI tools to predict recidivism, determine bail, and suggest sentences based on data points like socioeconomic status and family background. While acknowledging potential benefits—citing a Tulane University study showing AI recommendations increased the likelihood of low-risk offenders avoiding incarceration for certain crimes—the author emphasizes the significant legal and ethical challenges. The primary concern is the potential for racial and socioeconomic bias embedded in the training data, which can lead to discriminatory outcomes disguised as objective calculations.

The analysis notes that legal groups are beginning to challenge these practices in court, arguing that the opacity of proprietary algorithms violates a defendant's due process rights. The reliance on historical crime data, which reflects systemic biases, risks perpetuating inequalities within the justice system. The post concludes that while AI offers efficiency, its implementation in sentencing requires rigorous scrutiny and legal safeguards to ensure that automated "risk scores" do not infringe upon constitutional protections or unfairely disadvantage specific demographic groups.