The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Situating methods in the magic of Big Data and AI
Abstract
“Big Data” and “artificial intelligence” have captured the public imagination and are profoundly shaping social, economic, and political spheres. Through an interrogation of the histories, perceptions, and practices that shape these technologies, we problematize the myths that animate the supposed “magic” of these systems. In the face of an increasingly widespread blind faith in data-driven technologies, we argue for grounding machine learning-based practices and untethering them from hype and fear cycles. One path forward is to develop a rich methodological framework for addressing the strengths and weaknesses of doing data analysis. Through provocatively reimagining machine learning as computational ethnography, we invite practitioners to prioritize methodological reflection and recognize that all knowledge work is situated practice.
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“Elish and boyd (2018) say that 'suggesting that a technology 'works like magic' reinforces 'a sense that how the technology works is unknowable' (Selbst, quoted in (Elish and boyd, 2018)).”
From AI Séance: Recounts from designing artificial intelligence for transcendence, interpretive lenses and chance (2025) · cites this paper as Elish and boyd, 2018
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Self-citationModel Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science
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'The plan is just survival': Data Work in Kenya and the Regime of Entrapment
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“…AI studies scholarship has devoted extensive attention to the harms that surface at the point of deployment [13, 37, 117], an emerging line of research has begun to interrogate the infrastructures [16, 81], labor [43, 51, 120], and organizational processes that make AI systems possible [82, 111].”
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Metadata
- Authors
- Elish, M. C., & boyd, D.
- Venue
- Communication Monographs
- Publisher
- Informa UK Limited
- Published
- 2017-09-19
- Volume
- 85
- Issue
- 1
- Pages
- 57-80
- ISSN
- 0363-7751 (ISSN-L)
- 1479-5787
- OpenAlex
- W2755246076
- Cited by
- 409
- References
- 12
- FWCI
- 50.01
- Type
- journal article
- Language
- en
- Open access
- closed
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