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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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“Other researchers also voice concerns regarding challenges, threats, and costs associated with ADM transparency including the potential loss of privacy of the data subjects, attracting fraudulent behavior, loss of competitive advantage [24], complexity associated with systems that change over time [25], unclarity onto…”
BackgroundZilka, M., Ashurst, C., Chambers, L., Goodman, E., Ugwudike, P., & Oswald, M. (2023) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3617694.3623246 · Cited by 1
“In order to ensure that the tools employed by the police are trustworthy, improving Trans-parency is merely a first step: transparency mechanisms alone are no guarantee of meaningful engagement or accountability [2, 24].”
MethodAFTEA Framework for Supporting Dynamic Autonomous Driving Situation
Kim, S., Kang, J., & Yoon, Y.-I. (2024) · Electronics · doi:10.3390/electronics13173535 · Cited by 1
Michel, S., Bidan, M., & Gerbaix, S. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708575 · Cited by 1
“Others [13] [16]; [32] stress that an organization should take responsibility for their algorithms regardless of how opaque they are [33].”
BackgroundReviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems
Cobbe, J., Lee, M. S. A., & Singh, J. P. (2021) · Apollo (University of Cambridge) · doi:10.17863/cam.65493 · Cited by 1
Tinkering: A Way Towards Designing Transparent Algorithmic User Interfaces.
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Seizing the Networked Crime: Legal Framework for the Governance of Social Media Crimes in India
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Legal Issues of Big Data Application in the Russian Federation
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Planners navigating platform urbanism
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Data: The Premise of New Governance
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Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0005
Conditions of the Right to Erasure
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0004
Open Questions on Balancing in the GDPR
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0007
The Right to Erasure in Practice
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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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