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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Cites
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How the machine “thinks”: Understanding opacity in machine learning algorithms
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BackgroundBarocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Big Data, new epistemologies and paradigm shifts
Kitchin, R. (2014) · Big Data & Society · doi:10.1177/2053951714528481 · Cited by 2,355
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What computers still can't do: a critique of artificial reason
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In AI we trust? Perceptions about automated decision-making by artificial intelligence
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00931-w · Cited by 1,038
Governing artificial intelligence: ethical, legal and technical opportunities and challenges
Cath, C. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0080 · Cited by 745
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581
Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022) · Annals of Operations Research · doi:10.1007/s10479-020-03683-9 · Cited by 419
Impact of artificial intelligence on employees working in industry 4.0 led organizations
Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2022) · International Journal of Manpower · doi:10.1108/ijm-03-2021-0173 · Cited by 416
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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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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