Flipping the Script on Criminal Justice Risk Assessment: An actuarial model for assessing the risk the federal sentencing system poses to defendants

Meyer, Martha J.; Horowitz, Aaron; Marshall, Erica M.; Lum, Kristian

doi:10.1145/3531146.3533104

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Scholarship on predictive technologies in the public sector has exploded in recent years [2,9,11,18,28,32]. The use of algorithmic decision-making in the criminal legal system has been particularly controversial, with reason.…”

Section: Background and Related Work 21 Risk Assessment Instruments A...mentioning

confidence: 99%

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

Pruss

2023

2023 ACM Conference on Fairness, Accountability, and Transparency

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Recidivism risk assessment instruments are presented as an 'evidencebased' 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 presentence 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.

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Section: Background and Related Work 21 Risk Assessment Instruments A...mentioning

confidence: 99%

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

Pruss

2023

2023 ACM Conference on Fairness, Accountability, and Transparency

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The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool

Gerchick,

Jegede,

Shah

et al. 2023

2023 ACM Conference on Fairness, Accountability, and Transparency

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Improving Group Fairness Assessments with Proxies

Harvey,

Lee,

Singh

2024

ACM J. Responsib. Comput.

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Although algorithms are increasingly used to guide real-world decision-making, their potential for propagating bias remains challenging to measure. A common approach for researchers and practitioners examining algorithms for unintended discriminatory biases is to assess group fairness, which compares outcomes across typically sensitive or protected demographic features like race, gender, or age. In practice, however, data representing these group attributes is often not collected, or may be unavailable due to policy, legal, or other constraints. As a result, practitioners often find themselves tasked with assessing fairness in the face of these missing features. In such cases, they can either forgo a bias audit, obtain the missing data directly, or impute it. Because obtaining additional data is often prohibitively expensive or raises privacy concerns, many practitioners attempt to impute missing data using proxies. Through a survey of the data used in algorithmic fairness literature, which we make public to facilitate future research, we show that when available at all, most publicly available proxy sources are in the form of summary tables , which contain only aggregate statistics about a population. Prior work has found that these proxies are not predictive enough on their own to accurately measure group fairness. Even proxy variables that are correlated with group attributes also contain noise (i.e. will predict attributes for a subset of the population effectively at random). Here, we outline a method for improving accuracy in measuring group fairness using summary tables. Specifically, we propose improving accuracy by focusing only on highly predictive values within proxy variables, and outline the conditions under which these proxies can estimate fairness disparities with high accuracy. We then show that a major disqualifying criterion—an association between the proxy and the outcome—can be controlled for using causal inference. Finally, we show that when proxy data is missing altogether, our approach is applicable to rule-based proxies constructed using subject-matter context applied to the original data alone. Crucially, we are able to extract information on group disparities from proxies that may have low discriminatory power at the population level. We illustrate our results through a variety of case studies with real and simulated data. In all, we present a viable method allowing the assessment of fairness in the face of missing data, with limited privacy implications and without needing to rely on complex, expensive, or proprietary data sources.

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Flipping the Script on Criminal Justice Risk Assessment: An actuarial model for assessing the risk the federal sentencing system poses to defendants

Cited by 3 publications

References 37 publications

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

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

The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool

Improving Group Fairness Assessments with Proxies

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