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Blame Avoidance Strategies and the Use of Algorithms in Public Policy Decision Making. A Case Study on Criminal Justice Policies in the US

Jurisprudence
Decision Making
Empirical
Policy-Making
Georg Wenzelburger
Saarland University
Georg Wenzelburger
Saarland University

Abstract

The increased use of algorithmic decision-making (ADM) systems has spurred a debate about the chances and risks involved. Optimists emphasize that algorithms are capable of recognizing patterns in enormous amounts of data and do not suffer from the psychological biases that plague human decision-making (Kahneman 2012). In contrast, critical voices ring the alarm bells emphasizing the opaqueness of algorithms (Ananny/Crawford 2018; Pasquale 2016), potentially in-built biases (Angwin et al. 2016) and legal concerns (Yeung 2018). While all these debates on the risks and chances of ADM use are important, we know surprisingly little about the real-life implementation of ADM systems. In fact, there are only very few studies that analyze how decision-makers use ADM and how this changes their behavior. As van der Voort et al. (2019) argue, most analyses “neglect the institutions that shape the process from data generation to the decisions taken”. This is problematic because the consequences of ADM systems are at least as dependent on the implementation in an actual decision-making context as on their technical features (Zweig et al. 2018). Only if we know how ADM systems work in real life conditions can we assess chances and risks of ADM use (see also Veale et al. 2018). To address this shortcoming of the current literature, our article provides a case study on how risk assessment software based on AI has been implemented in the Criminal Justice system in Eau Claire County (WI, USA). The county is one of the front-runner regions of “evidence-based decision-making” in Criminal Justice and has introduced the software COMPAS to provide risk assessment for pre-trial and post-trial decisions. The case therefore provides a perfect illustration on how ADM systems affect decision-making on the ground. Based on a close reading of primary source material and qualitative expert interviews, we show how COMPAS has been introduced to provide risk assessments of offenders. Our findings indicate that the main appeal of using the ADM system comes from two sources. First, decisions in the Criminal Justice system are often taken under high uncertainty while at the same time carrying the potential of far-reaching consequences. In this situation, gaining evidence about statistical correlations delivered by software substantially reduces uncertainty. It changes the basic characteristics from a situation of fundamental uncertainty to one of statistical risk (Mousavi/Gigerenzer 2014). Therefore, even though decision-makers sometimes only possess incomplete knowledge about the inner workings of an ADM system, they are very open to using it. Second, using software-based evidence provides a possibility for decision-makers to avoid blame for decisions that may have harmful consequences for society. While it may create problems of accountability and responsibility (Ananny/Crawford 2018; Veale et al. 2018), it is – from an actor’s perspective – an instrument of blame shifting (Hood 2011). These findings from our case study reveal that it is at least equally important to think about the consequences the use of ADM systems has for the broader decision-making context as to evaluate the quality of their technical features.