It's Not Privacy, and It's Not Fair

By Cynthia Dwork, D. Mulligan. 2013

113 citations recorded by the source corpus.

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Citations in the corpus

Fair decision making using privacy-protected data (2019, background): “Dwork and Mulligan [15] warn against the expectation that privacy controls and transparency alone can offer resistance to discrimination in the context of large-scale data collection and automated classification.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “As a result, both scientists (Dwork and Mulligan, 2013; Barocas and Selbst, 2016) and journalists (Hill, 2014; Kirchner, 2015) have pressed for greater scrutiny of the use of social data against possible ethical pitfalls, such as breaching users privacy (Goroﬀ, 2015), or enabling racial,…”
Where are human subjects in Big Data research? The emerging ethics divide (2016, background): “How terms of service deﬁne ‘‘public’’ can be very diﬀerent from how actual human subjects conduct publicness in practice, which complicates the computational measures and personal eﬀorts required to protect privacy (Brunton and Nissenbaum, 2015; Dwork, 2011; Dwork and Mulligan, 2013).”
Ethical Implications and Accountability of Algorithms (2018, unknown): “…of algorithms used in advertising, hiring, lending, risk assessment, etc. Hidden behind the apron of these headlines lies a tension between the idea that algorithms are neutral and organic when “the reality is a far messier mix of technical and human curating” (Dwork and Mulligan 2013, p. 35).”
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI (2020, background): “, [30, 34, 38, 39, 43, 78, 79]), these findings underscore the need for thorough, empirically grounded research on how AI ethics principles can be effectively operationalized by practitioners throughout the development and deployment lifecycle.”
Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, background): “…argued for “greater attention to the values embedded and reﬂected in classiﬁcations, and the roles they play in shaping public and private life,” observing digital analytics “promised— or threatened—to bring classiﬁcation to an increasing range of human activity” (Dwork & Mulligan, 2013, p. 35).”
Big data, bigger dilemmas: A critical review (2015, background): “Humans who have always been the measured subjects receive an opportunity to “own” their data, challenge the metrics, or even expose values and biases embedded in automated classifications and divisions (Dwork & Mulligan, 2013).”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “Other authors have also argued for the need to define policy [54].”
Privacy for All: Ensuring Fair and Equitable Privacy Protections (2018, background): “We are by no means the first to consider these concerns and their possible relationship; Dwork and Mulligan (2013) argue for a shifting the focus of legal analysis of information systems from privacy and transparency to other social factors including fairness.”
Social Injustice in Surveillance Capitalism (2017, background): “…of privacy infringements is insufficient, and it provides a distraction from other serious social harms that can occur when people and their data are separated (see Newman 2015; Lyon 2003a; Crawford 2014; Stalder 2002; Lyon 2007; Zuboff 2016; Dwork and Mulligan 2013; Andrejevic 2014; Matzner 2014).”
Translation Tutorial : A Shared Lexicon for Research and Practice in Human-Centered Software Systems (2018, unknown): “(Dwork and Mulligan, 2013).”