Chapter: "Blue Data" (Excerpt from "The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement")

Ferguson, Andrew Guthrie

doi:10.2139/ssrn.3959202

Cited by 28 publications

(43 citation statements)

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“…Belangrijke oorzaken hiervan zijn de toegenomen technische mogelijkheden om grote hoeveelheden data te verzamelen, te bewerken en te analyseren, maar ook de mogelijkheden om de uitkomsten ervan in het politiewerk te gebruiken (o.a. WRR, 2016;Ferguson, 2017;Smith, Bennett Moses & Chan, 2018;Rienks & Schuilenburg, 2020;Brayne, 2021). Daarnaast spelen politieke en culturele ontwikkelingen hierbij een belangrijke rol, van de politieke wil om bepaalde vormen van criminaliteit in een steeds vroegtijdiger stadium aan te pakken tot een sterk geloof in techniek als 'de' oplossing voor maatschappelijke problemen zoals onveiligheid (Morozov, 2013;Das & Schuilenburg, 2020).…”

Section: Inleidingunclassified

“…Zo wordt in het geval van predictive policing onder meer gewezen op de risico's van bureaucratisering, false negatives and positives, selffulfilling prophecies, discriminatie van minderheden, en het identificeren van risicogroepen op basis van bepaalde kenmerken en categorieën (o.a. Ferguson, 2017;Peeters & Schuilenburg, 2018;Spithoven & Beerends, 2019;Rienks & Schuilenburg, 2020;O'Neil, 2016). Ook rechtsstatelijke zorgen worden in dit verband regelmatig geuit, variërend van het probleem van vuile data, function creep in de vorm van het hergebruik van data voor andere doelen dan oorspronkelijk vastgelegd, fishing expeditions, spurious correlations waarbij onterecht oorzakelijke verbanden worden gelegd, tot de schending van het onschuldbeginsel (o.a.…”

Section: Inleidingunclassified

“…In de wetenschappelijke literatuur over big data-toepassingen in het veiligheidsdomein gaat de aandacht uit naar de bijdrage die deze toepassingen kunnen leveren aan de opsporing en het voorkomen van criminaliteit en overlast. Het meest genoemde en tot de verbeelding sprekende voorbeeld hiervan is predictive policing (Ferguson, 2017;Hardyns & Rummens, 2018;Brayne, 2021). 5 Daarbij wordt geprobeerd te voorspellen wat de kans is op criminaliteit of regelovertreding in een specifiek gebied gedurende een bepaalde periode om daar vervolgens de surveillance-inzet van de politie op af stemmen.…”

Section: Werk Op Straatunclassified

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Big data in het veiligheidsdomein: onderzoek naar big data-toepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie

Schuilenburg¹,

Soudijn²

2021

Tijdschrift Voor Veiligheid

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Section: Inleidingunclassified

Section: Werk Op Straatunclassified

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Big data in het veiligheidsdomein: onderzoek naar big data-toepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie

Schuilenburg¹,

Soudijn²

2021

Tijdschrift Voor Veiligheid

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“…Multiple (non)-governmental organisations are concerned about bias in low enforcement applications and fear that predictive methods may target minority communities. There are studies that show the existence of the racial bias in different public safety applications such as pedestrian stops [144], racial profiling of vehicles [99], use of force [66], and drug enforcement and arrests [171]. The work in [221] investigated the existence of racial bias in predictive policing.…”

Section: Crime and Public Safetymentioning

confidence: 99%

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Hamdi¹,

Shaban²,

Erradi³

et al. 2021

Preprint

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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…This means considering how, as Jason Edward Lewis notes, “The bias in these systems is not a bug but rather a feature of an interlocking set of knowledge systems designed over centuries to benefit white men first and foremost” (Lewis, 2021 ). Proponents of this shift from bias to power focus on how search engines are imbricated in systems of race and can therefore perpetuate racism (Noble, 2018 ); how AI is shaped by colonialism and the subsequent need for a decolonial approach to AI (Mohamed et al, 2020 ); how racial hierarchies shape the way that AI is illustrated and imagined (Cave & Dihal, 2020 ); and how AI perpetuates an unjust criminal justice system and dramatically expands carceral regimes into spheres of intimate and daily life (Ferguson, 2017 ; Wang, 2018 ; Benjamin, 2019a , 2019b ; Schwerzmann, 2021 ). We push forward this body of scholarship by showing how the rapid growth of the field of recruitment AI fails to meaningfully reckon with what race and gender are and how they are embedded in recruitment AI systems, which holds serious implications for the anti-bias claims of these technologies.…”

Section: Introductionmentioning

confidence: 99%

Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”

Drage

Mackereth

2022

Philos. Technol.

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In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward.

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Chapter: "Blue Data" (Excerpt from "The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement")

Cited by 28 publications

References 0 publications

Big data in het veiligheidsdomein: onderzoek naar big data-toepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie

Big data in het veiligheidsdomein: onderzoek naar big data-toepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”

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