The Relevance of Algorithms

By Tarleton Gillespie, Pablo Boczkowski. 2013

1,673 citations recorded by the source corpus.

Subfields: History and Philosophy of Science, Safety Research, Sociology and Political Science, Philosophy

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Citations in the corpus

How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016, background): “(Gillespie, 2012) The goal of this article was to look more deeply into machine learning algorithms and the nature of their ‘opacity’, relating this to sociological interests in classi-ﬁcation and discrimination.”
Towards Algorithmic Experience: Initial Efforts for Social Media Contexts (2018, background): “In another article, the same author considers “public relevance algorithms” and suggests six provisional dimensions as a way to delimit these [14]: 1.”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “With such massive streams of relevant data to mine and train algorithms with, as well as increased analytical and technical capacities, it is of no surprise that companies and public sector actors are turning to machine learning-based algorithms to tackle complex problems at the limits of human deci”
Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture (2018, positive): “This field is particularly important, as digital platforms and software algorithms are creating ever larger assemblages of data to inform decision-making (Gillespie 2014; O’Neil, 2016).”
The challenges and limits of big data algorithms in technocratic governance (2016, background): “The veneer that because algorithm are mathematically driven and data centric, the outputs are impartial and unambiguous (Boyd & Crawford, 2012; Crawford, 2013) has been challenged (Gillespie, 2014) considering the economic and cultural imperatives of how algorithm influencing the wayswe consume cult”
Down the (White) Rabbit Hole: The Extreme Right and Online Recommender Systems (2015, background): “…the only social media site to be criticized for some of the outcomes of its recommendation practices and the ‘‘calculated publics’’ they ‘‘help to constitute and codify . . . [P]ublics that would not otherwise exist except that the algorithm called them into existence’’ (Gillespie, 2014, p. 189).”
In AI we trust? Perceptions about automated decision-making by artificial intelligence (2020, neutral): “These perceptions may be consistent with the socio-technical discourse (by technology companies, among others) that tries to bring forward an image of lack of bias, objectivity, neutrality, consistency, and predictability of outcomes of algorithmic processes (Gillespie 2014) despite increasing evide”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “Data have been discussed as “the new oil” (Tarnoff 2017; Thorp 2012) that organizations need to extract and monetize using algorithms or sets of defined steps structured to process data (Gillespie 2014).”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “In a social media context, algorithmic curation—automated selection and ranking of content— acts as a gatekeeper, defining what is relevant, knowable, and authoritative [27, 6, 23].”
Terms of inclusion: Data, discourse, violence (2020, neutral): “The landscape is further complicated by data processed algorithmically—that is, through processes and procedures of software that turn data inputs into particular outputs (Bucher, 2018; Gillespie, 2014).”
Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram (2018, background): “Even so, algorithms play an important role in structuring our online experiences (Beer, 2009; Bucher, 2012; Cheney-Lippold, 2011; Gillespie, 2014).”
Algorithmic IF … THEN rules and the conditions and consequences of power (2017, methodology): “Taken together with recent studies of algorithms in Science and Technology Studies (STS) (Gillespie, 2011, 2014; Neyland, 2015) and geography (Kitchin, 2014), this suggests we need to understand the algorithm-in-action as situated (Suchman, Randall, & Blom-berg, 2002) among a variety of people,…”
Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed (2015, background): “It is difﬁcult to evaluate algorithms in isolation from their context of use—without data from real users to act upon, one cannot get an accurate picture of performance [13, 21].”