Algorithmic Sentencing and Fairness Perceptions
TL;DR
It is found that even when AI is fully explainable and used only to support human judges, it significantly reduces perceived fairness compared to human-only decisions and with a lack of human involvement and when decisions are non-explainable.
Abstract
We study how citizens perceive the fairness of using artificial intelligence (AI) in criminal sentencing, using a survey experiment with a representative sample in Norway ( N = 2222 ). Participants were randomly assigned to one of four experimental vignettes describing the use of AI in judicial decision-making that varied along two dimensions: human involvement (decision support vs. fully automated decision-making) and explainability (whether it is possible to determine which factors the algorithm gives the most weight to or not). In addition, respondents could be assigned to a fifth baseline condition representing the status quo, in which sentencing decisions were made solely by human judges. We find that even when AI is fully explainable and used only to support human judges, it significantly reduces perceived fairness compared to human-only decisions. This fairness gap widens with a lack of human involvement (i.e., fully automated AI system) and when decisions are non-explainable. Our results highlight that the mere involvement of AI in legal decision-making can undermine public fairness perceptions, regardless of its technical merits.