The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics
Abstract
This paper explores how political theory may help us map algorithmic logics against different visions of the political. Drawing on Chantal Mouffe’s theories of agonistic pluralism, this paper depicts algorithms in public life in ten distinct scenes, in order to ask the question, what kinds of politics do they instantiate? Algorithms are working within highly contested online spaces of public discourse, such as YouTube and Facebook, where incompatible perspectives coexist. Yet algorithms are designed to produce clear “winners” from information contests, often with little visibility or accountability for how those contests are designed. In isolation, many of these algorithms seem the opposite of agonistic: much of the complexity of search, ranking, and recommendation algorithms is nonnegotiable and kept far from view, inside an algorithmic “black box.” But what if we widen our perspective? This paper suggests agonistic pluralism as both a design ideal for engineers and a provocation to understand algorithms in a broader social context: rather than focusing on the calculations in isolation, we need to account for the spaces of contestation where they operate.
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Cites
A Contextual Approach to Privacy Online
Nissenbaum, H. (2011) · Daedalus · doi:10.1162/daed_a_00113 · Cited by 649
Research works printed but not linked: 19
- Connolly William, 1999, Why I Am Not A Secularist
- Crawford Kate, Tarleton Gillespie. 2014. “What is a Flag For? Social Media Reporting Tools and The Vocabulary of Complaint.” New Media & Society, July 15, 2014, doi: 10.1177/1461444814543163, http://nms.sagepub.com/content/early/2014/07/15/1461444814543163. · DOI · Paper page
- 10.7551/mitpress/8732.001.0001 · DOI
- Galloway Alexander R., 2013, The Interface Effect
- Gillespie Tarleton, 2013, Media Technologies, 167
- 10.7208/chicago/9780226321370.001.0001 · DOI · Paper page
- 10.1111/j.1467-856x.2007.00308.x · DOI
- 10.1007/s10676-006-9133-z · DOI · Paper page
- 10.1177/030631299029003004 · DOI · Paper page
- 10.1177/0263276409350283 · DOI · Paper page
- Mouffe Chantal, 1999, Social Research, 66, 745
- Mouffe Chantal, 2003, The Return of the Political
- Mouffe Chantal, 2013, Agonistics: Thinking the World Politically
- Pinch Trevor. 2011. “Book Reviewing for Amazon.com: How Socio-technical Systems Struggle to Make Less from More.” In Managing Overflow in Affluent Societies, edited by Czarniawska Barbara, Löfgren Orvar, 68–87. New York: Routledge.
- Sedgwick Eve Kosofsky, 2004, Touching Feeling: Affect, Pedagogy, Performativity, 93
- Tomkins Silvan S., 2008, Affect Imagery Consciousness: The Complete Edition
- 10.1017/CBO9781139170888 · DOI · Paper page
- 10.1057/palgrave.cpt.9300091 · DOI
- Winner Langdon, 1980, Daedalus, 109, 121
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Metadata
- Authors
- Crawford, K.
- Venue
- Science, Technology, & Human Values
- Publisher
- SAGE Publications
- Published
- 2015-06-24
- Volume
- 41
- Issue
- 1
- Pages
- 77-92
- ISSN
- 0162-2439 (ISSN-L)
- 1552-8251
- OpenAlex
- W2317166805
- Cited by
- 314
- References
- 1
- FWCI
- 55.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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