Digital prediction technologies in the justice system: The implications of a ‘race-neutral’ agenda

Abstract

This article critically analyses the nexus of race and risk prediction technologies applied in justice systems across western jurisdictions. There is mounting evidence that the technologies are overpredicting the risk of recidivism posed by racialized groups, particularly black people. Yet the technologies are ostensibly race neutral in the sense that they do not refer explicitly to race. They are also compliant with race equality laws. To investigate how apparently race neutral technologies can nevertheless yield racially disparate outcomes, the article draws on insights distilled from the sociology of race and the sociological scholarship on standardization. It uses themes from these two scholarships to unravel the intersecting structural and creational dynamics capable of fomenting the digital racialization of risk.

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

“…Aligned with this was the view that the developers of commercial algorithms were being empowered to exercise the ‘sovereign power’ previously reserved for the state. As others have noted, this provides further impetus for non-state actors to participate in criminal justice policy and governance (Hannah-Moffat, 2018; Ugwudike, 2020). It calls attention to the politics of science and technology innovation since, as noted earlier, the developers exert power through their ability to inform technology design and criminal justice expenditures (Jasanoff, 2015).…”

“…Another issue dealt with by oppositional frames is the role of data-driven algorithms and their developers in knowledge production about social problems such as crime and risk. In drawing attention to this, oppositional frames echoed the concerns expressed by others who note that data-driven technologies, including the commercial versions, are arbitrarily reframing constructions of crime and deviance (Eaglin, 2017; Ugwudike, 2020). Here, again we witness how the concept of sociotechnical imaginaries can highlight the politics of science and technology innovation.…”

Predictive Algorithms in Justice Systems and the Limits of Tech-Reformism

“…The CAS scholarship also draws attention to the digital racialisation of risk (Ugwudike 2020). This concept offers insights into a key implication of algorithmic bias, such as the over-prediction of risk in cases involving some minorities.…”

“…Creators of predictive algorithms are currently equipped with the power to infuse algorithms with their values, choices and preferences. Examples include the selection of foundational or training data, the construction of prediction parameters and the choice of fairness metrics, all of which can inject subjective values into algorithmic data processing and outputs (Ugwudike 2020;Eaglin 2017). It is argued that access to digital capital, particularly the power to use digital technologies to influence or even dominate knowledge production, can be linked to intersecting social categories of race, gender and class (e.g., Benjamin 2019;Noble 2018;van Dijk 2005).…”

International Criminology and International Connectivity: Reflections at a Pivotal Juncture

“…On one hand, the rise of corporate-driven big data systems—high-velocity, high-volume and high-variety mass data extraction and analysis practices of increasing sophistication and geographical reach—brings opportunities for international criminological research (see Wall, 2018 ). But big data still carry with them risks concerning algorithmic bias and verifiability (see, e.g., Ugwudike, 2020 ; Miller, 2014 ). On the other hand, with regard to conventional data from official sources, there seems little prospect that government reluctance to ensure the timely and full public release of inconvenient crime data (van Dijk, 2008 ) will dissipate any time soon.…”