Who errs? Algorithm aversion, the source of judicial error, and public support for self-help behaviors

Abstract

ABSTRACT People often turn to self-help behaviors when formal processes of the state deteriorate, becoming inaccessible or ineffective. This deterioration can often include real or alleged inaccuracies in the courts that lower trust and confidence in the judicial system. Increasingly, one potential source of error in the courts is algorithmic, with more and more facets of the judicial system incorporating actuarial assessments. In this paper, I examine whether trust and confidence, separate from legitimacy, and the source of judicial error – humans or algorithms – matter for declared support of self-help behaviors, such as naming and shaming on social media, protesting, and violent economic protesting. In the experiment, respondents read information about identical levels of judicial error made by either a human or algorithm. They then indicated their attitudes towards the judicial systems and self-help behaviors. Respondents that read about algorithm-error had greater odds of supporting some self-help behaviors. In addition, the level of trust in the courts, and not legitimacy, mattered most for support of self-help behaviors. The paper discusses potential mechanisms behind the differences between the human- and algorithmic-error groups as well as the distinction between trust and legitimacy for self-help behaviors.