The social power of algorithms

By David Beer. 2017

568 citations recorded by the source corpus.

ABSTRACT This article explores the questions associated with what might be thought of as the social power of algorithms. The article, which introduces a special issue on the same topic, begins by reflecting on how we might approach algorithms from a social scientific perspective. The article is then split into two sections. The first deals with the issues that might be associated with an analysis of the power of the algorithms themselves. This section outlines a series of issues associated with the functionality of the algorithms and how these functions are powerfully deployed within social world. The second section then focuses upon the notion of the algorithm. In this section, the article argues that we need to look beyond the algorithms themselves, as a technical and material presence, to explore how the notion or concept of the algorithm is also an important feature of their potential power. In this section, it is suggested that we look at the way that notions of the algorithm are evoked as a part of broader rationalities and ways of seeing the world. Exploring the notion of the algorithm may enable us to see how algorithms also play a part in social ordering processes, both in terms of how the algorithm is used to promote certain visions of calculative objectivity and also in relation to the wider governmentalities that this concept might be used to open up.

Subfields: Cognitive Neuroscience, Computer Science Applications, Safety Research

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Citations in the corpus

The ethics of AI in health care: A mapping review. (2020, background): “As Beer (2017) attests, when thinking about the power of an al-gorithm, we need to think beyond the impact and consequences of the code, to the powerful ways in which notions and ideas about the al-gorithm circulate throughout the social world.”
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019, background): “As work in critical algorithm studies has pointed out, transparency practices do not take place in a social vacuum but play particular roles in their speciﬁc cultural and organizational settings (Beer, 2017; Kemper and Kolkman, 2018).”
Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, background): “Logan, 2017).”
Toward Fair, Accountable, and Transparent Algorithms: Case Studies on Algorithm Initiatives in Korea and China (2019, background): “The great potential of an algorithm-driven economy is broadly acknowledged, and there is mounting enthusiasm and pressure to achieve the vision of an algorithm-based society (Beer 2017).”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “Recent applications of algorithm-based HR decision-making differ from traditional electronic monitoring in at least three ways (Beer 2017; Weibel et al. 2016; Zarsky 2015).”
Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, background): “While numerous conceptual studies have explored how algorithms shape everyday life (e.g., Beer, 2017; Diakopoulos, 2015; Willson, 2017), these typically take a technological perspective to uncover how algorithms exert power.”
Governing artificial intelligence: ethical, legal and technical opportunities and challenges (2018, background): “Beer [39] aims to focus the debate on the ‘social power of algorithms’.”
Algorithmic risk assessment policing models: lessons from the Durham HART model and ‘Experimental’ proportionality (2018, background): “16 Algorithms in policing have ‘ outcomes in mind ’ and are likely to result in ‘ recursive processes as those outcomes are modelled back into algorithm design.”
Ethics as Methods: Doing Ethics in the Era of Big Data Research—Introduction (2018, background): “This Special Issue contributes specific cases and fine-grained conceptual distinctions to ongoing discussions and critiques of data-driven research models and big data analytics (cf. Beer, 2017; boyd & Crawford, 2012; Cheney-Lippold, 2017; Leurs, 2017; Zook et al., 2017).”
The ethics of algorithms: key problems and solutions (2020, background): “(Ananny and Crawford 2018; Beer 2017; Taddeo and Floridi 2018b; Möller et al.”