:Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age

By Lucia Zedner. 2008

334 citations recorded by the source corpus.

Subfields: Demography, Sociology and Political Science, Statistics and Probability

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Citations in the corpus

Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “For example, Harcourt shows that if the population changes their behavior in response to changing decision probabilities, prediction-based decisions can backfire and diminish social welfare [51].”
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse (2019, background): “…especially automated moderation of online content (e.g., Gillespie, 2012, 2017); criminal justice, especially in policing and sentencing (e.g., Harcourt, 2006; Rosenblat, Randhava, boyd, Gangadharan, & Yu, 2014a); employment, especially hiring and surveillance (e.g., Ajunwa, 2019; Barocas &…”
Decision‐making under uncertainty in child protection: Creating a just and learning culture (2018, methodology): “A “ratchet effect” where such biases get bigger has been found in the criminal justice system when such algorithms are used (Harcourt, 2008).”
A Qualitative Exploration of Perceptions of Algorithmic Fairness (2018, background): “To raise awareness and illustrate the potential for wide-ranging consequences, researchers and the press have pointed out a number of specific instances of algorithmic unfairness [19,58], for example, in predictive policing [19,43], the online housing marketplace [27,28], online ads [13,17,20,29,82]”
Algorithmic surveillance: the collection conundrum (2017, methodology): “Sophisticated investigatory programmes use machine learning processes in an effort to predict individual criminality based on algorithms (Eligon and Williams 2015; Harcourt 2007; Rich 2015 – 2016, 876).”
An impact assessment of machine learning risk forecasts on parole board decisions and recidivism (2017, background): “The most salient concerns are typically ethical (Harcourt 2008) and jurisprudential (Starr 2014).”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “First, workers have collectively resisted algorithmic control by engaging in public critique of algorithmic systems , criticizing how algorithms could lead to the reproduction or reinforcement of social and racial inequalities because of biased training data (Harcourt, 2007; O ’ Neil, 2016).”
Big Data Surveillance: The Case of Policing (2017, background): “That said, although actuarial methods have existed in corrections and the courts for almost a century (Feeley and Simon 1992; Harcourt 2006; Lyon 2003), data-driven decision-making has become systematically incorporated into law enforcement practices only in recent decades.”
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “We also would draw attention to larger challenges with predictive systems: that they might not achieve social or policy goals at all by their nature (Harcourt, 2006), or that fairness might not be the most relevant issue as much as ideas of stigmatisation, over-surveillance, or the devaluing of…”
Risk Technology in Sentencing: Testing the Promises and Perils (Commentary on Hannah-Moffat, 2011) (2013, background): “Similarly, Harcourt (2007) argues that the variable heavily emphasized in risk assessment tools——criminal history——is a proxy for being black.”
Risk as a Proxy for Race (2015, neutral): “ a. Race as an early predictor of parole failure The first parole prediction instrument that was ever used in the parole decisionmaking process—the “Burgess method” developed in 1927 and 1928 by Professor Ernest Burgess at the University of Chicago—included the nationali”
Surveillance, Snowden, and Big Data: Capacities, consequences, critique (2014, background): “As other studies have indicated, such invidious categories not only persist but are also ampli-ﬁed as greater reliance is placed on automated (see e.g. Gandy, 2012) and actuarial (see e.g. Harcourt, 2007) methods.”
Engaging rational discrimination: exploring reasons for placing regulatory constraints on decision support systems (2010, neutral): “92 Harcourt (2007).”
Algorithms in practice: Comparing web journalism and criminal justice (2017, background): “This is where predictive risk-assessment tools come in. Quantitative models are far from new in criminal justice and indeed have existed for most of the twentieth century (Harcourt, 2006).”
Actuarial Sentencing: An “Unsettled” Proposition (2013, background): “It is certainly important to question and validate the technical efficiency of a risk-measurement tool, but this approach simply invites improvements or technical refinements and does not tackle the underlying question of whether the tool is justifiable (Zedner, 2008).”
Can we avoid reductionism in risk reduction? (2017, background): “…are suspicious that contemporary extensions of risk assessment and risk reduction will likely only reproduce, or may even exacerbate, the injustices of contemporary criminal justice policy under a more “objective” guise; and will fail even on their own terms at reducing crime (Harcourt, 2007).”