The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The Challenges of AI-Decision-Making in Government and Administrative Law: A Proposal for Regulatory Design
Open accessAbstract
Artificial Intelligence (“AI”) decision-making is rapidly becoming a key technology in government. AI decision-making has a number of significant benefits. These include reduced costs, consistency in decision-making, and the potential for reduced bias. It is not, however, an unalloyed good. There are a number of risks associated with the technology. For example, it may have inherent biases or be prone to developing certain biases. These issues are most evident when AI is used to deal with vulnerable populations, populations unaware of or less able to deal with advanced technologies and sophisticated systems in general. With these populations, AI has the potential to do significant harm. Governments have struggled to understand and address AI decision-making appropriately. And nowhere is this clearer than in dealing with vulnerable populations. This Article addresses these issues using theories of effective regulation and two case studies of AI decision-making in Australia. It proposes two regulatory innovations: a default advocate for negative decisions “Golden Rule”, and a monitoring body that, among other things, implements Professor Aziz Huq’s proposed “right to a well-calibrated decision.”
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“Attribution remains essential, since public bodies retain legal responsibility even where private vendors supply or maintain algorithmic tools (Cobbe and Singh, 2020; Sheehy and Ng, 2024).”
From Judicial review of algorithmic administrative systems legality evidence and remedies in the smart city state (2026) · cites this paper as Sheehy and Ng, 2024
In the citing paper
“Administrative effectiveness and rights protection are not mutually exclusive within public law, provided that systems are designed and governed with review in mind (Sheehy and Ng, 2024; Haim, 2023).”
From Judicial review of algorithmic administrative systems legality evidence and remedies in the smart city state (2026) · cites this paper as Sheehy and Ng, 2024
Citations
Cites
Tikkinen-Piri, C., Rohunen, A., & Markkula, J. (2018) · Computer Law & Security Review · doi:10.1016/j.clsr.2017.05.015 · Cited by 460
Cobbe, J. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3226913 · Cited by 165
Procurement As Policy: Administrative Process for Machine Learning
Mulligan, D. K., & Bamberger, K. A. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3464203 · Cited by 75
Cited by
Sorted by citations
Ameen, K. S., Towfiq, T. A., Mousa, K. M., & Pooyanmehr, M. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1802986 · Cited by 4
“Proposals for “reviewable” automated decision-making suggest that accountability should be grounded in documentation, system logs, traceable decision pathways, and clear duties assigned to public bodies, even when external tools are involved (Cobbe and Singh, 2020; Sheehy and Ng, 2024).”
InfluentialAdvantages and Risks of Artificial Intelligence and Automated Decision-making in Administrative Law
Akimovska Maletić, I. (2026) · Collection Regional Law Review · doi:10.56461/iup_rlrc.2026.7.ch2
Metadata
- Authors
- Sheehy, B., & Ng, Y.-F.
- Affiliations
- University of Canberra (AU)
- Venue
- Indiana Law Review
- Publisher
- IU Indianapolis University Library
- Published
- 2024-06-10
- Volume
- 57
- Issue
- 3
- Pages
- 665-699
- ISSN
- 0090-4198 (ISSN-L)
- 2169-320X
- OpenAlex
- W4400115313
- Cited by
- 7
- References
- 3
- Citations by year
- 2026: 3 · 2025: 4
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Indiana Law Review (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Law › Administrative Law and Governance
- Keywords
- AI decision-making, administrative law, algorithmic bias, vulnerable populations, golden rule, algorithmic accountability, regulatory design
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