Algorithms as culture: Some tactics for the ethnography of algorithmic systems

By Nick Seaver. Big Data & Society, 2017

757 citations recorded by the source corpus.

This article responds to recent debates in critical algorithm studies about the significance of the term “algorithm.” Where some have suggested that critical scholars should align their use of the term with its common definition in professional computer science, I argue that we should instead approach algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. This approach builds on the work of Laura Devendorf, Elizabeth Goodman, and Annemarie Mol. Different ways of enacting algorithms foreground certain issues while occluding others: computer scientists enact algorithms as conceptual objects indifferent to implementation details, while calls for accountability enact algorithms as closed boxes to be opened. I propose that critical researchers might seek to enact algorithms ethnographically, seeing them as heterogeneous and diffuse sociotechnical systems, rather than rigidly constrained and procedural formulas. To do so, I suggest thinking of algorithms not “in” culture, as the event occasioning this essay was titled, but “as” culture: part of broad patterns of meaning and practice that can be engaged with empirically. I offer a set of practical tactics for the ethnographic enactment of algorithmic systems, which do not depend on pinning down a singular “algorithm” or achieving “access,” but which rather work from the partial and mobile position of an outsider.

Subfields: Cultural Studies, Safety Research, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/312d71a4deec097f02b923a5215cab313cf132f5

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Citations in the corpus

Algorithms at Work: The New Contested Terrain of Control (2020, background): “…(2016) notes, however, “ since algorithms arise in practice in relation to other computational forms, such as data structures, they need to be analyzed and understood within those systems of relation that give them meaning and animate them ” (see also Christin, 2019; Seaver, 2017; Ziewitz, 2016).”
Managing visibility on YouTube through algorithmic gossip (2019, background): “The first investigates ‘technical’ sites, such as recommender systems and tech organisations, to examine how algorithms are constructed (e.g. Dourish, 2016; Hallinan and Striphas, 2016; Seaver, 2017; Vonderau, 2019).”
This Thing Called Fairness (2019, background): “Similarly, anthropologist Nick Seaver argues that algorithms are cultural objects situated in sociotechnical systems, constructed as much socially as technically [135].”
ORES (2019, background): “Geiger humanistic and interpretivist social science perspective (e.g. [2, 24]. This literature also emphasizes a need to see algorithmic systems as dynamic and constantly under revision by developers [39] — work that is invisible in most platforms, but is foregrounded in ORES. In these case studies,”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “Yet, current algorithm-based HR decision-making tools go far beyond the monitoring activities described in the electronic monitoring literature (Ananny 2016; Dourish 2016; Seaver 2017).”
Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020, background): “In this way, we want to highlight how datasets work as culture— that is, “not [as] singular technical objects that enter into many different cultural interactions, but... rather [as] unstable objects, culturally enacted by the practices people use to engage with them” [Seaver, 2017].”
Art and the science of generative AI (2023, background): “Generative AI systems are diffuse, sociotechnical systems [18] and therefore more work is needed to understand how people reason about the complex interplay between human actors and computational processes.”
Terms of inclusion: Data, discourse, violence (2020, background): “The author would like to thank Sareeta Amrute, Marika Cifor, Megan Finn, Daniel Greene, Alex Hanna, Luke Stark, Tonia Sutherland, Nick Seaver, as well as all attendees of the Feminist Data Workshop (University of Michigan, August 2019) for feedback and formative conversations that directly informed ”
Data Science as Political Action: Grounding Data Science in a Politics of Justice (2018, neutral): “Political scientist Harold Lasswell describes politics as “who gets what, when, how”[24].”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, neutral): “…foundation of AI-enabled systems, are in effect socio-technical systems embedded within and across cultures with different meanings and points of interaction (Seaver, 2017) and self-regulation as a way to satisfy a moral imperative over a formal legal one is insufficient (Wieringa, 2020).”
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse (2019, background): “This implicit assumption, as Nick Seaver (2017) notes, recognizes that data and algorithms both shape and are shaped by cultural context, but it casts them as akin to a rock in a stream: ‘ the rock is not part of the stream, though the stream may jostle and erode it and the rock may produce ripples…”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, methodology): “As noted above, algorithms are basically instructions fed to a computer [70, p. 16], They are technical constructs that are simultaneously deeply social and cultural [125].”
Race After Technology: Abolitionist Tools for the New Jim Code (2020, background): “In this regard, Seaver (2017) has proposed tactics to enact algorithms ethnographically, which help approach them as rich sociotechnical systems rather than constrained and procedural formulas.”
Algorithmic realism: expanding the boundaries of algorithmic thought (2020, background): “the varied practices that people use to engage with them” [133].”
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019, background): “As Seaver (2017: 1) argues, algorithms can be understood ‘‘as culture,’’ as ‘‘heterogenous and diﬀuse sociotechnical systems, rather than rigidly constrained and procedural formulas.’’”
Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, background): “This makes their logics difficult to entangle, even for highly skilled engineers (Burrell, 2016; Seaver, 2017).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “Increasing the complexity of the analysis further, Nick Seaver (2017) highlights that the very attempt at making algorithms transparent and accountable will change the social scene within which they operate.”
Ethical Implications and Accountability of Algorithms (2018, neutral): “Algorithms are viewed as maximizing efficiency or accuracy; computer scientists are, therefore, responsible for ensuring efficiency and accuracy (Seaver 2017).”