Algorithms and their others: Algorithmic culture in context

By Paul Dourish. Big Data & Society, 2016

361 citations recorded by the source corpus.

Algorithms, once obscure objects of technical art, have lately been subject to considerable popular and scholarly scrutiny. What does it mean to adopt the algorithm as an object of analytic attention? What is in view, and out of view, when we focus on the algorithm? Using Niklaus Wirth's 1975 formulation that “algorithms + data structures = programs” as a launching-off point, this paper examines how an algorithmic lens shapes the way in which we might inquire into contemporary digital culture.

Subfields: Computer Science Applications, History and Philosophy of Science

View Paper: https://www.semanticscholar.org/paper/f2ee9f867d04c8c2482ffffe94e2cfe2f169a1f7

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Citations in the corpus

Algorithmic management in a work context (2021, background): “A defining feature of algorithmic management is the data which fuels the predictive modeling techniques, with many acknowl-edging that the political economy of data capture is a significant driver in transforming labor norms (Dourish, 2016; Newlands, 2020; Shestakofsky, 2017).”
Algorithms as culture: Some tactics for the ethnography of algorithmic systems (2017, background): “If we want to understand engineers and get them to listen to us, then we need to use terms as they do; it is a matter ‘‘of ethnographic responsibility and practical politics’’ (Dourish, 2016: 2).”
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).”
Algorithmic realism: expanding the boundaries of algorithmic thought (2020, background): “The divergent meanings of “algorithms” within critical discourse and computer science [37] re ects the di erences between algorithms in theory and the “algorithmic”
Algorithms at Work: The New Contested Terrain of Control (2020, background): “In addition, when employer payment algorithms changed wages rapidly (Lee et al., 2015; Shapiro, 2018), workers often did not know why they were experiencing the pay changes and had limited recourse to find out (Rahman, 2017; Raval & Dourish, 2016; Schwartz, 2018b).”
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020, background): “Dourish [14] adds scrutability as an important component of interactivity.”
Managing visibility on YouTube through algorithmic gossip (2019, methodology): “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).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “…and diffuse sociotechnical systems” (Seaver 2017, p. 1), instantiated and imbued with meaning through their embeddedness in those technologies, and with their various and often fluid and evolving roles merging technical and cultural practices (Dourish 2016; Roberge and Seyfert 2016; Seaver 2017).”
Towards Algorithmic Experience: Initial Efforts for Social Media Contexts (2018, background): “…and situation of use; Dourish describes how the material manifestations of algorithms are shaped by the “specific instantiation – as a running system, running in a particular place, on a particular computer, connected to a particular network, with a particular hardware configuration” [11:5].”