The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Transparent to whom? No algorithmic accountability without a critical audience
Open accessAbstract
Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the ‘data value chain’, yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce established ways of thinking, and may favour some political orientations over others. This is a cause for concern and calls for more transparency in the development, implementation, and use of algorithms in public- and private-sector organizations. We argue that one elementary – yet key – question remains largely undiscussed. If transparency is a primary concern, then to whom should algorithms be transparent? We consider algorithms as socio-technical assemblages and conclude that without a critical audience, algorithms cannot be held accountable.
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“Crucially, a meaningful account cannot be given unless the forum can understand and critically engage with the subject matter [37,75].”
From Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems (2021) · cites this paper as [37]
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Cites
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Cited by
Sorted by citations
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882
(2019) · doi:10.1080/1369118x.2019.1573914 · Cited by 719
Role of fairness, accountability, and transparency in algorithmic affordance
Shin, D., & Park, Y. J. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.04.019 · Cited by 643
Towards transparency by design for artificial intelligence
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Algorithms as work designers: How algorithmic management influences the design of jobs
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Metadata
- Authors
- Kemper, J., & Kolkman, D.
- Affiliations
- University of Amsterdam (NL)
- Eindhoven University of Technology (NL)
- Corresponding author
- Kemper, Jakko
- Venue
- Information, Communication & Society
- Publisher
- Informa UK Limited
- Published
- 2018-06-18
- Volume
- 22
- Issue
- 14
- Pages
- 2081-2096
- ISSN
- 1369-118X (ISSN-L)
- 1468-4462
- OpenAlex
- W2809386502
- Cited by
- 260
- References
- 66
- Citations by year
- 2026: 26 · 2025: 26 · 2024: 30 · 2023: 36 · 2022: 32 · 2021: 51 · 2020: 39 · 2019: 19 · 2018: 1
- FWCI
- 54.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Communication & Society (publishedVersion, cc-by-nc-nd)
- UvA-DARE (University of Amsterdam) (publishedVersion, cc-by-nc-nd)
- TU/e Research Portal (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- algorithmic accountability, algorithmic transparency, critical audiences, algorithmic decision-making, algorithmic bias, data value chain, sociotechnical assemblages, organizational decision-making, big data, political orientation, public and private institutions
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