Privacy at the Margins

Skinner-Thompson, Scott

doi:10.1017/9781316850350

Cited by 23 publications

(20 citation statements)

References 74 publications

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“…In this section, we outline two major understandings of privacy, namely, “privacy as commodity or an element of property rights” (Skinner‐Thompson, 2020, p. 10) and privacy as “human right” (Bygrave, 1998), and how neither adequately addresses individual data privacy rights. (Also see Bygrave, 1998).…”

Section: Current/main Understandings Of Privacy and Their (Technologi...mentioning

confidence: 99%

“…Skinner‐Thompson (2020) refers to “marginal privacy”—how “seemingly benign regulatory laws and background norms” informing privacy in different international contexts insufficiently protect those in certain intersectional demographic groups. There is extensive evidence documenting the “degree to which marginalised communities experience less lived privacy” and are subject to “greater degrees of surveillance, and feel the burdens of surveillance more acutely” (p. 16).…”

Section: Five Alternative Considerations Of Personal Data and Privacymentioning

confidence: 99%

“…Of these three options, behavioural use represents by far the biggest source of revenue for technology companies" (Viljoen, 2021, p. 13). Skinner-Thompson (2020) argues that "before a person can even trade away their information, they must be deemed to control that information in the first instance" (p. 10; italics in the original). As Taylor et al (2016) assert, "it becomes increasingly difficult for an individual to be aware of every data processing activity that might include their data, to assess in how far the processing is done legitimately and if not, to request the data controller to stop their activities" (p. 15).…”

Section: Personal Data and Privacy As Commoditymentioning

confidence: 99%

“…Those who define categories assume the right to 'name things' and as Morphy (2016) and others (eg, Kukutai & Taylor, 2016) point out, indigenous peoples are claiming 'back' the right to 'name things' through the data sovereignty. Skinner-Thompson (2020) refers to "marginal privacy"-how "seemingly benign regulatory laws and background norms" informing privacy in different international contexts insufficiently protect those in certain intersectional demographic groups. There is extensive evidence documenting the "degree to which marginalised communities experience less lived privacy" and are subject to "greater degrees of surveillance, and feel the burdens of surveillance more acutely" (p. 16).…”

Section: Indigenous Understandings Of Personal Data and Privacymentioning

confidence: 99%

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The answer is (not only) technological: Considering student data privacy in learning analytics

Prinsloo

Slade

Khalil

2022

Brit J Educational Tech

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Evidence shows that appropriate use of technology in education has the potential to increase the effectiveness of, eg, teaching, learning and student support. There is also evidence that technology can introduce new problems and ethical issues, eg, student privacy. This article maps some limitations of technological approaches that ensure student data privacy in learning analytics from a critical data studies (CDS) perspective. In this conceptual article, we map the claims, grounds and warrants of technological solutions to maintaining student data privacy in learning analytics. Our findings suggest that many technological solutions are based on assumptions, such as that individuals have control over their data (‘data as commodity’), which can be exchanged under agreed conditions, or that individuals embrace their personal data privacy as a human right to be respected and protected. Regulating student data privacy in the context of learning analytics through technology mostly depends on institutional data governance, consent, data security and accountability. We consider alternative approaches to viewing (student) data privacy, such as contextual integrity; data privacy as ontological; group privacy; and indigenous understandings of privacy. Such perspectives destabilise many assumptions informing technological solutions, including privacy enhancing technology (PET). What is already known about this topic Various actors (including those in higher education) have access to and collect, use and analyse greater volumes of personal (student) data, with finer granularity, increasingly from multiplatforms and data sources. There is growing awareness and concern about individual (student) privacy. Privacy enhancing technologies (PETs) offer a range of solutions to individuals to protect their data privacy. What this paper adds A review of the assumption that technology provides adequate or complete solutions for ensuring individual data privacy. A mapping of five alternative understandings of personal data privacy and its implications for technological solutions. Consideration of implications for the protection of student privacy in learning analytics. Implications for practice and/or policy Student data privacy is not only a technological problem to be solved but should also be understood as a social problem. The use of PETs offers some solutions for data privacy in learning analytics. Strategies to protect student data privacy should include student agency, literacy and a whole‐system approach.

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Section: Current/main Understandings Of Privacy and Their (Technologi...mentioning

confidence: 99%

Section: Five Alternative Considerations Of Personal Data and Privacymentioning

confidence: 99%

Section: Personal Data and Privacy As Commoditymentioning

confidence: 99%

Section: Indigenous Understandings Of Personal Data and Privacymentioning

confidence: 99%

See 2 more Smart Citations

The answer is (not only) technological: Considering student data privacy in learning analytics

Prinsloo

Slade

Khalil

2022

Brit J Educational Tech

View full text Add to dashboard Cite

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“…Chair, 2020). Historians of privacy have been exploring past connections between feminism, liberation and privacy (Igo, 2018), and American scholars keep active exploring privacy's potential to counter the oppression of women, queer folks, and racial and religious minorities (Skinner-Thompson, 2020).…”

Section: Introduction: Distinguishing Privacy and Data Protectionmentioning

confidence: 99%

Feminist data protection: an introduction

Theilen¹,

Baur²,

Bieker³

et al. 2021

Internet Policy Review

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Feminist data protection' is not an established term or field of study: data protection discourse is dominated by doctrinal legal and economic positions, and feminist perspectives are few and far between. This editorial introduction summarises a number of recent interventions in the broader fields of data sciences and surveillance studies, then turns to data protection itself and considers how it might be understood, critiqued and possibly reimagined in feminist terms. Finally, the authors return to 'feminist data protection' and the different directions in which it might be further developed-as a feminist approach to data protection, as the protection of feminist data, and as a feminist way of protecting data-and provide an overview of the papers included in the present special issue.

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Predatory predictions and the ethics of predictive analytics

Martin

2023

Asso for Info Science & Tech

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In this paper, I critically examine ethical issues introduced by predictive analytics. I argue firms can have a market incentive to construct deceptively inflated true‐positive outcomes: individuals are over‐categorized as requiring a penalizing treatment and the treatment leads to mistakenly thinking this label was correct. I show that differences in power between firms developing and using predictive analytics compared to subjects can lead to firms reaping the benefits of predatory predictions while subjects can bear the brunt of the costs. While profitable, the use of predatory predictions can deceive stakeholders by inflating the measurement of accuracy, diminish the individuality of subjects, and exert arbitrary power. I then argue that firms have a responsibility to distinguish between the treatment effect and predictive power of the predictive analytics program, better internalize the costs of categorizing someone as needing a penalizing treatment, and justify the predictions of subjects and general use of predictive analytics. Subjecting individuals to predatory predictions only for a firms' efficiency and benefit is unethical and an arbitrary exertion of power. Firms developing and deploying a predictive analytics program can benefit from constructing predatory predictions while the cost is borne by the less powerful subjects of the program.

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Privacy at the Margins

Cited by 23 publications

References 74 publications

The answer is (not only) technological: Considering student data privacy in learning analytics

The answer is (not only) technological: Considering student data privacy in learning analytics

Feminist data protection: an introduction

Predatory predictions and the ethics of predictive analytics

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