Arna Woemmel (U Hamburg Business) et al. have posted “Public Attitudes Toward Algorithmic Risk Assessments In Courts: A Deliberation Experiment” on SSRN. Here is the abstract:
We study public attitudes toward algorithmic risk assessment tools in the criminal justice system using an online deliberation study with 2,358 UK participants and apply quantitative text analysis to identify key topics, biases, and sentiments underlying these attitudes. Participants were presented with a scenario about algorithmic tools used for early release decisions and then randomly assigned in groups of three to deliberate on the scenario via free-form messenger chats. The scenarios varied between subjects, but not within groups, in three algorithmic features: (i) inclusion vs. exclusion of discriminatory variables in the tool’s input data, (ii) development by private vs. public institutions, and (iii) full vs. limited judicial discretion over the tool. Prior to group deliberation, the majority approved of these tools, with particularly high approval for tools developed by public institutions or allowing full judicial discretion. However, deliberation significantly reduced approval in all treatment groups and diminished the effects of information treatments, leading to a convergence of attitudes across groups. Text analysis suggests a negativity bias in the deliberation process, with arguments against the tools (e.g., algorithmic bias) showing stronger associations with attitude changes than supportive arguments (e.g., cost savings), even though both types of arguments were equally present in the discussions. These findings highlight the malleability of stated public approval for these tools, especially when they are deliberated in greater depth.