Automating administrative burden in algorithmic criminal record expungement

Abstract Administrative burden describes the learning costs, psychological costs, and compliance costs people face when attempting to interface with the government, particularly in seeking a benefit. Algorithmic and automated processes offer the potential of reducing administrative burdens, but scant empirical research has determined to what, if any effect. This study uses the case of criminal record expungement in two policy contexts: traditional, court petition-based systems and newly enacted automated systems, to understand if and how administrative burden persists, and whether and how these burdens operate differently in the context of the criminal legal system. Drawing on interviews with 105 expungement-eligible people, we find that while automated expungement schemes shift the burden from petitioner to state to initiate the process, automation inadvertently creates new administrative burdens via failure to notify, partial clearances, and opaque data processes. Furthermore, respondents described how automation failed to provide a sense of confirmation from the state that their sentence was truly completed, rehabilitation had been acknowledged, or that collateral consequences should no longer wield the same power. Overall, we argue that leveraging automation to reduce burdens must include information availability by design; otherwise policy reforms may fail to fully achieve their goals.