Ghosting the Machine: Judicial Resistance to a Recidivism Risk Assessment Instrument

TL;DR

This study reveals that judges in Pennsylvania largely ignore a recidivism risk assessment tool due to organizational factors and resistance, highlighting challenges in implementing AI-based criminal justice reforms.

Contribution

It demonstrates that organizational norms and dissemination issues, not distrust or automation fears, drive resistance to algorithmic tools in criminal justice.

Findings

Judges dismiss the instrument as 'useless' and 'not helpful'

Organizational factors influence non-use more than individual distrust

Algorithm reforms often fail to produce desired impacts

Abstract

Recidivism risk assessment instruments are presented as an 'evidence-based' strategy for criminal justice reform - a way of increasing consistency in sentencing, replacing cash bail, and reducing mass incarceration. In practice, however, AI-centric reforms can simply add another layer to the sluggish, labyrinthine machinery of bureaucratic systems and are met with internal resistance. Through a community-informed interview-based study of 23 criminal judges and other criminal legal bureaucrats in Pennsylvania, I find that judges overwhelmingly ignore a recently-implemented sentence risk assessment instrument, which they disparage as "useless," "worthless," "boring," "a waste of time," "a non-thing," and simply "not helpful." I argue that this algorithm aversion cannot be accounted for by individuals' distrust of the tools or automation anxieties, per the explanations given by existing scholarship. Rather, the instrument's non-use is the result of an interplay between three organizational factors: county-level norms about pre-sentence investigation reports; alterations made to the instrument by the Pennsylvania Sentencing Commission in response to years of public and internal resistance; and problems with how information is disseminated to judges. These findings shed new light on the important role of organizational influences on professional resistance to algorithms, which helps explain why algorithm-centric reforms can fail to have their desired effect. This study also contributes to an empirically-informed argument against the use of risk assessment instruments: they are resource-intensive and have not demonstrated positive on-the-ground impacts.