Democratic governance in an age of datafication: Lessons from mapping government discourses and practices

Redden, Joanna

doi:10.1177/2053951718809145

Cited by 57 publications

(46 citation statements)

References 51 publications

(51 reference statements)

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“…Our research captured data on some institutional and organizational practices which indicated how data systems in child welfare are continuously negotiated, repurposed, advanced and resisted. This is in line with previous work identifying the awareness and work ongoing among public sector workers concerning the limits of datafied systems (Veale, Van Kleek, and Binns 2018;Redden 2018). Although knowledge or evidence of the extent to which the introduction of data systems changes how decisions are made and actions taken remains a significant gap, our research pointed to the status of data systems in relation to domain-specific expertise and professionalism as a continuous source of tension.…”

Section: Governmentalities Legalities and Questions About Rightssupporting

confidence: 89%

Datafied child welfare services: unpacking politics, economics and power

2020

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This article analyses three distinct child welfare data systems in England. We focus on child welfare as a contested area in public services where data systems are being used to inform decisionmaking and transforming governance. We advance the use of "data assemblage" as an analytical framework to detail how key political and economic factors influence the development of these data systems. We provide an empirically grounded demonstration of why child welfare data systems must not be considered neutral decision aid tools. We identify how systems of thought, ownership structures, policy agendas, organizational practices, and legal frameworks influence these data systems. We find similarities in the move toward greater sharing of sensitive data, but differences in attitudes toward public-private partnerships, rights and uses of prediction. There is a worrying lack of information available about the impacts of these systems on those who are subject to themparticularly in relation to predictive data systems. We argue for policy debates to go beyond technical fixes and privacy concerns to engage with fundamental questions about the power dynamics and rights issues linked to the expansion of data sharing in this sector as well as whether predictive data systems should be used at all.

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Section: Governmentalities Legalities and Questions About Rightssupporting

confidence: 89%

Datafied child welfare services: unpacking politics, economics and power

2020

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“…Given the ever-growing relevance of big data and automated systems in all areas of society, many argue that we have entered an age of "datafication" (e.g., Gray et al, 2018;Hintz et al, 2018;Mayer-Schönberger & Cukier, 2013;Redden, 2018). With private companies and governments worldwide collecting and analyzing vast amounts of-amongst others-personal data and the use of automated systems increasing rapidly, there has been a "profound transformation in how society is ordered, decisions are made, and citizens are monitored through 'big data'" (Hintz et al, 2018, p. 2f).…”

Section: Introductionmentioning

confidence: 99%

Critical big data literacy tools—Engaging citizens and promoting empowered internet usage

Sander

2020

Data & Policy

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Datafied societies need informed public debate about the implications of data science technologies. At present, internet users are often unaware of the potential consequences of disclosing personal data online and few citizens have the knowledge to participate in such debates. This paper argues that critical big data literacy efforts are one way to address this lack of knowledge. It draws on findings from a small qualitative investigation and discusses the effectiveness of online critical big data literacy tools. Through pre and post use testing, the short- and longer-term influence of these tools on people’s privacy attitudes and behavior was investigated. The study’s findings suggested that the tools tested had a predominantly positive initial effect, leading to improved critical big data literacy among most participants, which resulted in more privacy-sensitive attitudes and internet usage. When analyzing the tools’ longer-term influence, results were more mixed, with evidence suggesting for some that literacy effects of the tools were short-lived, while for others they led to more persistent and growing literacy. The findings confirm previous research noting the complexity of privacy attitudes and also find that resignation toward privacy is multi-faceted. Overall, this study reaffirms the importance of critical big data literacy and produces new findings about the value of interactive data literacy tools. These tools have been under-researched to date. This research shows that these tools could provide a relevant means to work toward empowering internet users, promoting a critical internet usage and, ideally, enabling more citizens to engage in public debates about changing data systems.

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“…The widespread adoption of automated decision-making algorithms (AADM 1 ), powered by AI, analytics, and big data, in human services in sectors vital for any societysuch as social welfare, education, healthcare, employment, public housing, policing and criminal justiceis motivated and justified by intended and expected positive effects. These include, for example, increased efficiency and speed of service delivery, better compliance with government policies, greater transparency and accountability, reduction of costs and, most importantly for service beneficiaries, improved overall service quality (Redden, 2018;Caplan et al 2018;Alston, 2019aAlston, , 2019bPark and Humphry, 2019). However, in spite of positive intentions, there is growing and disturbing evidence of the harmful societal effects of AADM (see for example O'Neil, 2016a; Eubanks, 2018;Caplan, et al, 2018;Park and Humphry, 2019;Benjamin, 2019;Alston, 2019aAlston, , 2020; UN, 2020).…”

Section: Introductionmentioning

confidence: 99%

Algorithmic pollution: Making the invisible visible

Marjanovic

Ćećez-Kecmanović

Vidgen

2021

Journal of Information Technology

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In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim - and provide evidence - that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor or an analogy, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community.

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Democratic governance in an age of datafication: Lessons from mapping government discourses and practices

Cited by 57 publications

References 51 publications

Datafied child welfare services: unpacking politics, economics and power

Datafied child welfare services: unpacking politics, economics and power

Critical big data literacy tools—Engaging citizens and promoting empowered internet usage

Algorithmic pollution: Making the invisible visible

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