Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say
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Angwin, Julia; Larson, Jeff

doi:10.1201/9781003278290-38

Cited by 37 publications

(24 citation statements)

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“…But their reliance on questionable data calls into question their potential to address these problems, particularly those related to data harms. As already noted, several studies point to the intractability of such harms (Angwin and Larson, 2016; Hao and Stray, 2019; Lum and Isaac, 2016; Starr, 2014). Therefore, the frames may evoke positive sociotechnical imaginaries that nevertheless obscure the well-documented social costs of data harms, particularly the reproduction of historical inequalities such as systemic biases and the negative labelling of affected groups (Eaglin, 2017; Ugwudike, 2020).…”

Section: What Explains the Different Frames Identified?mentioning

confidence: 93%

“…This can be triggered by predictive policing algorithms that are prompted by racially biased arrest data to designate their geographical region as ‘high crime’ and in need of enhanced policing activity (Ferguson, 2017; Harcourt, 2015; Lum and Isaac, 2016). Minorities are also more vulnerable than others to overprediction of recidivism risks by algorithms relying on similar data (Angwin and Larson, 2016; Eaglin, 2017; Hannah-Moffat, 2018; Hao and Stray, 2019; Ugwudike, 2020).…”

Section: The Critical Scholarship On Data-driven Technologiesmentioning

confidence: 99%

“…There is a fast-growing body of critical literature on the proliferation of datafication in the form of the data-driven models now increasingly applied in justice systems across the world for predicting risk (Angwin and Larson, 2016), forecasting crime hotspots (Ensign et al, 2018; Lum and Isaac, 2016) and implementing the biometric identification of targeted individuals (Bennett Moses and Chan, 2018; Fussey, 2019). The rapid rise of data-driven technologies in justice systems highlights their growing popularity as prime crime control tools despite the growing corpus of studies pointing to their potential harms (Barocas and Selbst, 2016; Ferguson, 2017; Fussey, 2019; Hao and Stray, 2019; Oswald et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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The datafication revolution in criminal justice: An empirical exploration of frames portraying data-driven technologies for crime prevention and control

Lavorgna

Ugwudike

2021

Big Data & Society

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The proliferation of big data analytics in criminal justice suggests that there are positive frames and imaginaries legitimising them and depicting them as the panacea for efficient crime control. Criminological and criminal justice scholarship has paid insufficient attention to these frames and their accompanying narratives. To address the gap created by the lack of theoretical and empirical insight in this area, this article draws on a study that systematically reviewed and compared multidisciplinary academic abstracts on the data-driven tools now shaping decision-making across several justice systems. Using insights distilled from the study, the article proposes three frames (optimistic, neutral, oppositional) for understanding how the technologies are portrayed. Inherent in the frames are a set of narratives emphasising their ostensible status as vital crime control mechanisms. These narratives obfuscate the harms of data-driven technologies and evince idealistic imaginaries of their capabilities. The narratives are bolstered by unequal structural arrangements, specifically the unevenly distributed digital capital with which some are empowered to participate in technology development for criminal justice application and other forms of penal governance. In unravelling these issues, the article advances current understanding of the dynamics that sustain the depiction of data-driven technologies as prime crime prevention and law enforcement tools.

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Section: What Explains the Different Frames Identified?mentioning

confidence: 93%

Section: The Critical Scholarship On Data-driven Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The datafication revolution in criminal justice: An empirical exploration of frames portraying data-driven technologies for crime prevention and control

Lavorgna

Ugwudike

2021

Big Data & Society

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“…The Conditional Distance Correlation (CDC) test (Wang et al, 2015) We apply our methods to a loan application dataset from a fintech company, the adult income dataset from UCI Machine Learning Repository 1 and the COMPAS recidivism data from ProPublica 2 (Angwin and Larson, 2016).…”

Section: Fairness Testmentioning

confidence: 99%

“…However, the community and the authorities have also raised concern that these automatically learned decisions may inherit the historical bias and discrimination from the training data and would cause serious ethical problems when used in practice (Nature Editorial, 2016;Angwin and Larson, 2016;Dwoskin, 2015; Executive Office of the President et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

On Learning and Testing of Counterfactual Fairness through Data Preprocessing

Chen¹,

Song²,

Ghosh³

2022

Preprint

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Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper, we develop the Fair Learning through dAta Preprocessing (FLAP) algorithm to learn counterfactually fair decisions from biased training data and formalize the conditions where different data preprocessing procedures should be used to guarantee counterfactual fairness. We also show that Counterfactual Fairness is equivalent to the conditional independence of the decisions and the sensitive attributes given the processed non-sensitive attributes, which enables us to detect discrimination in the original decision using the processed data. The performance of our algorithm is illustrated using simulated data and real-world applications.

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Handling Missing Values in Local Post-hoc Explainability

Cinquini,

Giannotti,

Guidotti

et al. 2023

Communications in Computer and Information Science

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Handling missing data is a significant challenge in the field of Machine Learning. While multiple Machine Learning algorithms can tackle this problem, there is a need for more transparent and interpretable approaches that can effectively handle missing data. As the development of understandable algorithms becomes increasingly essential, it is also crucial to have methods that can adequately manage them. This master's thesis project proposes an extended version of a widely used local and model-agnostic explainer that enables explainability in the presence of missing values. Extensive experiments show the effectiveness of the proposed method.

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Bias in Criminal Risk Scores Is Mathematically Inevitable, Researchers Say *

Cited by 37 publications

References 0 publications

The datafication revolution in criminal justice: An empirical exploration of frames portraying data-driven technologies for crime prevention and control

The datafication revolution in criminal justice: An empirical exploration of frames portraying data-driven technologies for crime prevention and control

On Learning and Testing of Counterfactual Fairness through Data Preprocessing

Handling Missing Values in Local Post-hoc Explainability

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