Recent efforts to understand the significance of precarious work have been limited in at least two important respects. One is the neglect of the ideological constructs that workers are led to embra...

Work and Identity in an Era of Precarious Employment: How Workers Respond to “Personal Branding” Discourse:

The number of predictive technologies used in the U.S. criminal justice system is on the rise. Yet there is little research to date on the reception of algorithms in criminal justice institutions. We draw on ethnographic fieldwork conducted within a large urban police department and a midsized criminal court to assess the impact of predictive technologies at different stages of the criminal justice process. We first show that similar arguments are mobilized to justify the adoption of predictive algorithms in law enforcement and criminal courts. In both cases, algorithms are described as more objective and efficient than humans’ discretionary judgment. We then study how predictive algorithms are used, documenting similar processes of professional resistance among law enforcement and legal professionals. In both cases, resentment toward predictive algorithms is fueled by fears of deskilling and heightened managerial surveillance. Two practical strategies of resistance emerge: footdragging and data obfuscation. We conclude by discussing how predictive technologies do not replace, but rather displace discretion to less visible—and therefore less accountable— areas within organizations, a shift which has important implications for inequality and the administration of justice in the age of big data. K E Y W O R D S : algorithms; prediction; policing; criminal courts; ethnography. In recent years, algorithms and artificial intelligence have attracted a great deal of scholarly and journalistic attention. Of particular interest is the development of predictive technologies designed to estimate the likelihood of a future event, such as the probability that an individual will default on a loan, the likelihood that a consumer will buy a specific product online, or the odds that a job candidate will have a long tenure in an organization. Predictive algorithms capture the imagination of scholars and journalists alike, in part because they raise the question of automated judgment: the replacement – or at least the augmentation – of human discretion by mechanical procedures. Nowhere are these The authors contributed equally and are listed alphabetically. We would like to thank the three anonymous reviewers, as well as the organizers and participants of the Willen Seminar at Barnard College in 2016, “Punishment, Society, and Technology” session of the LSA Annual Meeting in 2018, and “Innovations and Technology in Studies of Crime and Social Control” session of the ASA Annual Meeting in 2018 for their helpful comments and feedback. Please direct correspondence to Sarah Brayne at the Department of Sociology at the University of Texas at Austin, 305 E. 23rd Street, A1700, RLP 3.306, Austin, TX 78712; telephone (512) 475-8641; email sbrayne@utexas.edu. VC The Author(s) 2020. Published by Oxford University Press on behalf of the Society for the Study of Social Problems. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 1 Social Problems, 2020, 0, 1–17 doi: 10.1093/socpro/spaa004 Article D ow naded rom http/academ ic.p.com /socpro/advance-articleoi/10.1093/socpro/spaa004/5782114 by Snford U niersity user on 06 M arch 2020 questions more salient than in the context of criminal justice. Over recent decades, the U.S. criminal justice system has witnessed a proliferation of algorithmic technologies. Police departments now increasingly rely on predictive software programs to target potential victims and offenders and predict when and where future crimes are likely to occur (Brayne 2017; Ferguson 2017). Likewise, criminal courts use multiple predictive instruments, called “risk-assessment tools,” to assess the risk of recidivism or failure to appear in court among defendants (Hannah-Moffat 2018; Harcourt 2006; Monahan and Skeem 2016). Predictive technologies, in turn, raise many questions about fairness and inequality in criminal justice. On the positive side, advocates emphasize the benefits of using “smart statistics” to reduce crime and improve a dysfunctional criminal justice system characterized by racial discrimination and mass incarceration (Brantingham, Valasik, and Mohler 2018; Milgram 2012). On the negative side, critics argue that algorithms tend to embed bias and reinforce social and racial inequalities, rather than reducing them (Benjamin 2019; Eubanks 2018; O’Neil 2016). They note that predictive algorithms draw on variables or proxies that are unfair and may be unconstitutional (Ferguson 2017; Starr 2014). Many point out that predictive algorithms may lead individuals to be surveilled and detained based on crimes they have not committed yet, frequently comparing these technologies to the science-fiction story Minority Report by Philip K. Dick and its movie adaptation, which evoke a dystopian future. To date, studies of criminal justice algorithms share three main characteristics. First, existing work tends to focus on the construction of algorithms, highlighting the proprietary aspect of most of these tools (which are often built by private companies) and criticizing their opacity (Angwin et al. 2016; Pasquale 2015; Wexler 2017). Second, they tend to treat the criminal justice system as a monolith, lumping together the cases of law enforcement, adjudication, sentencing, and community supervision (O’Neil 2016; Scannell 2016). Third, and most importantly, most studies fail to analyze contexts of reception, implicitly assuming – usually without empirical data – that police officers, judges, and prosecutors rely uncritically on what algorithms direct them to do in their daily routines (Harcourt 2006; Hvistendahl 2016; Mohler et al. 2015; Uchida and Swatt 2013). In this article, we adopt a different perspective. Building on a growing body of literature that analyzes the impact of big data in criminal justice (Hannah-Moffat, Maurutto, and Turnbull 2009; Lageson 2017; Lum, Koper, and Willis 2017; Sanders, Weston, and Schott 2015; Stevenson and Doleac 2018), as well as existing ethnographic work on the uses of algorithms (Brayne 2017; Christin 2017; Levy 2015; Rosenblat and Stark 2016; Shestakovsky 2017), we focus on the reception of predictive algorithms in different segments of the criminal justice system. Drawing on two indepth ethnographic studies – one conducted in a police department and the other in a criminal court – we examine two questions. First, to what extent does the adoption of predictive algorithms affect work practices in policing and criminal courts? Second, how do practitioners respond to algorithmic technologies (i.e., do they embrace or contest them)? Based on this ethnographic material, this article provides several key findings. First, we document a widespread – albeit uneven – use of big data technologies on the ground. In policing, big data are used for both person-based and place-based predictive identification, in addition to risk management, crime analysis, and investigations. In criminal courts, multiple predictive instruments, complemented by digital case management systems, are employed to quantify the risk of the defendants. Second, similar arguments are used in policing and courts to justify the use of predictive technologies. In both cases, algorithms are presented as more rational and objective than “gut feelings” or discretionary judgments. Third, we find similar strategies of resistance, fueled by fears of experiential devaluation and increased managerial surveillance, among law enforcement and legal professionals—most importantly, foot-dragging and data obfuscation. Despite these resemblances, we document important differences between our two cases. In particular, law enforcement officers were under more direct pressure to use the algorithms, whereas the legal professionals under consideration were able to keep their distance and ignore predictive technologies without consequences, a finding we relate to the 2 Brayne and Christin D ow naded rom http/academ ic.p.com /socpro/advance-articleoi/10.1093/socpro/spaa004/5782114 by Snford U niersity user on 06 M arch 2020 distinct hierarchical structures and levels of managerial oversight of the police department and criminal court we compared. We conclude by discussing the implications of these findings for research on technology and inequality in criminal justice. Whereas the current wave of critical scholarship on algorithmic bias often leans upon technological deterministic narratives in order to make social justice claims, here we focus on the social and institutional contexts within which such predictive systems are deployed and negotiated. In the process, we show that these tools acquire political nuance and meaning through practice, which can lead to unanticipated or undesirable outcomes: forms of workplace surveillance and the displacement of discretion to less accountable places. We argue that this sheds new light on the transformations of police and judicial discretion – with important consequences for social and racial inequality – in the age of big data. D E C I S I O N M A K I N G A C R O S S A V A R I E T Y O F D O M A I N S As a growing number of daily activities now take place online, an unprecedented amount of digital information is being collected, stored, and analyzed, making it possible to aggregate data across previously separate institutional settings. Harnessing this rapidly expanding corpus of digitized information, algorithms – broadly defined here as “[a] formally specified sequence(s) of logical operations that provides step-by-step instructions for computers to act on data and thus automate decisions” (Barocas et al. 2014) – are being used to guide decision-making across institutional domains as varied as education, journalism, credit, and criminal justice (Brayne 2017; Christin 2018; Fourcade and Healy 2017; O’Neil 2016; Pasquale 2015). Advocates for algorithmic technologies argue that by relying on “unbiased” assessments, algorithms may help deploy resources more efficiently and objective

Technologies of Crime Prediction: The Reception of Algorithms in Policing and Criminal Courts

With evidence comprising three years of ethnographic research in child support courts and 125 in-depth interviews with formerly incarcerated fathers, the author shows how criminal justice and child...

https://www.journals.uchicago.edu/doi/pdf/10.1086/697580

Incarcerated Fatherhood: The Entanglements of Child Support Debt and Mass Imprisonment

The American incarceration rate, though recently stabilized, increased rapidly over the past half century. Today, compared with the 1970s, more than five times as many people spend time in prison annually (National Research Council [2014][1]; Wakefield and Uggen [2010][2]). The historically

Criminal Justice Contact and Inequality

Based on 17 months of ethnographic fieldwork and 45 in-depth interviews with formerly-incarcerated men, this article explores how former prisoners navigate criminal justice and welfare bureaucracies in their daily lives. Formerly-incarcerated men must repeatedly engage with parole, public assistance agencies, transitional housing facilities, and community-based service providers to maintain freedom and access food, shelter, and rehabilitative services. Accessing resources requires the men simultaneously to manage multiple, overlapping entanglements across a fragmented network of bureaucracies. This runaround exacerbates the stress of poverty, breeds distrust of state authorities, and, in some cases, precipitates recidivism. Former prisoners learned how to cope with the runaround by treating systems navigation as a full-time occupation, but these skills did not translate into long-term economic security. Most study participants recurrently cycled between low-wage jobs, transitional housing facilities, and public assistance programs for years after release. This article illustrates the need to theorize prisoner reentry as a process that unfolds across a network of criminal justice and welfare bureaucracies and demonstrates how formerly-incarcerated men experience citizenship not only through coercive encounters with the criminal justice system but also through their simultaneous entanglements with safety-net bureaucracies.

The Runaround: Punishment, Welfare, and Poverty Survival after Prison

The appropriation of “welfare stigma” or stereotypes about poor people's overreliance and abuse of public aid in two core criminal justice functions is examined: felony adjudication in a court system and space allocation in a jail. Through a comparative ethnographic study in which an abductive analysis of data (20 months of fieldwork) was used, we show that criminal justice gatekeepers utilize welfare stigma to create stricter eligibility criteria for due process in criminal courts and occupancy in jails. Specifically, the number of court appearances, motions, trials, jail beds, food, showers, and medical services is considered by professionals to be the benefits that individuals seek to access and abuse. Professionals view their role as preventing (rather than granting) access to these resources. The comparative nature of our data reveals that welfare stigma has interorganizational utility by serving two different organizational goals: It streamlines convictions in courts, which pulls defendants through adjudication, and conversely, it expands early release from jails, which pulls inmates out of the custody population. In the context of diminishing social safety nets, our findings have implications for understanding how discretion is exercised in an American criminal justice system increasingly tasked with the distribution of social services to the urban poor.

Interorganizational utility of welfare stigma in the criminal justice system

P020 Evaluation of TMEM16A as a modifier of the cystic fibrosis lung phenotype utilising cystic fibrosis patient-specific human-induced pluripotent stem cells

Primary ciliary dyskinesia (PCD) is a rare heterogenic genetic disorder associated with perturbed biogenesis or function of motile cilia. Motile cilia dysfunction results in diminished mucociliary clearance (MCC) of pathogens in the respiratory tract and chronic airway inflammation and infections successively causing progressive lung damage. Current approaches to treat PCD are symptomatic, only, indicating an urgent need for curative therapeutic options. Here, we developed an in vitro model for PCD based on human induced pluripotent stem cell (hiPSC)-derived airway epithelium in Air-Liquid-Interface cultures. Applying transmission electron microscopy, immunofluorescence staining, ciliary beat frequency and mucociliary transport measurements, we could demonstrate that ciliated respiratory epithelia cells derived from two PCD patient specific hiPSC lines carrying mutations in DNAH5 and NME5, respectively, recapitulate the respective diseased phenotype on a molecular, structural and functional level.

/pdf/primary-ciliary-dyskinesia-patient-specific-hipsc-derived-1q42xpgj.pdf

Primary Ciliary Dyskinesia Patient-Specific hiPSC-Derived Airway Epithelium in Air-Liquid Interface Culture Recapitulates Disease Specific Phenotypes In Vitro

Nicole Gonzalez Van Cleve

Papers

Interorganizational utility of welfare stigma in the criminal justice system

P020 Evaluation of TMEM16A as a modifier of the cystic fibrosis lung phenotype utilising cystic fibrosis patient-specific human-induced pluripotent stem cells

Primary Ciliary Dyskinesia Patient-Specific hiPSC-Derived Airway Epithelium in Air-Liquid Interface Culture Recapitulates Disease Specific Phenotypes In Vitro