‘Hypernudge’: Big Data as a mode of regulation by design
By Karen Yeung. The Social Power of Algorithms, 2017
602 citations recorded by the source corpus.
ABSTRACT This paper draws on regulatory governance scholarship to argue that the analytic phenomenon currently known as ‘Big Data’ can be understood as a mode of ‘design-based’ regulation. Although Big Data decision-making technologies can take the form of automated decision-making systems, this paper focuses on algorithmic decision-guidance techniques. By highlighting correlations between data items that would not otherwise be observable, these techniques are being used to shape the informational choice context in which individual decision-making occurs, with the aim of channelling attention and decision-making in directions preferred by the ‘choice architect’. By relying upon the use of ‘nudge’ – a particular form of choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives, these techniques constitute a ‘soft’ form of design-based control. But, unlike the static Nudges popularised by Thaler and Sunstein [(2008). Nudge. London: Penguin Books] such as placing the salad in front of the lasagne to encourage healthy eating, Big Data analytic nudges are extremely powerful and potent due to their networked, continuously updated, dynamic and pervasive nature (hence ‘hypernudge’). I adopt a liberal, rights-based critique of these techniques, contrasting liberal theoretical accounts with selective insights from science and technology studies (STS) and surveillance studies on the other. I argue that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints.
Subfields: Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/1cbe7243f3e91f95069020bdaf5fa753fb663439
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In Sociology and Political Science
Citations in the corpus
- The ethics of nudging: An overview (2020, background): “…underresearched area is “digital choice architecture,” that is, the way data-driven nudges in smartphones, social networks, websites, and so on steer us in evermore precise ways (Alfano, Carter, & Cheong, 2018; Susser, Roessler, & Nissenbaum, 2019; Weinmann, Schneider, & Brocke, 2016; Yeung, 2017).”
- Ethical Implications and Accountability of Algorithms (2018, neutral): “3 Similarly, algorithms act like design-based regulation (Yeung 2017) where algorithms can be used for the consistent application of legal and regulatory regimes (Thornton 2016, p.”
- A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs (2021, background): “Attracting most attention from AI ethicists are perhaps the social challenges, including bias in AI systems, the increased use of surveillance and a lack of privacy [50] as well as using the combination of AI and Big Data to influence and manipulate [51,52].”
- What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, methodology): “In other words, these are decision-guidance processes [141, p. 121].”