Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
Open accessAbstract
As the use of algorithmic systems in high-stakes decision-making increases, the ability to contest algorithmic decisions is being recognised as an important safeguard for individuals. Yet, there is little guidance on what 'contestability'--the ability to contest decisions--in relation to algorithmic decision-making requires. Recent research presents different conceptualisations of contestability in algorithmic decision-making. We contribute to this growing body of work by describing and analysing the perspectives of people and organisations who made submissions in response to Australia's proposed 'AI Ethics Framework', the first framework of its kind to include 'contestability' as a core ethical principle. Our findings reveal that while the nature of contestability is disputed, it is seen as a way to protect individuals, and it resembles contestability in relation to human decision-making. We reflect on and discuss the implications of these findings.
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Restricting explanations to technical solutions currently available through XAI may limit the grounds for contestations.
“restricting explanations to technical solutions that are currently available through XAI may limit the grounds for contestations [67].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [67]
The field of contestable AI is still growing and many guidelines on how to design for contestability are conceptual in nature.
“The feld of contestable AI is still growing [3] and many of the guidelines on how to design for contestability are conceptual in nature [3,44,67].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [67]
AI detection tools have little-to-no contestability.
“and with little-to-no contestability [45]”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [45]
Contestability is often invoked in ethics guidelines but remains under-specified, making it difficult to implement in real decision pipelines.
“They argue that contestability is often invoked in ethics guidelines, yet remains under-specified in ways that make it difficult to implement in real decision pipelines.”
From Designing “Contestability” in Automated HR Decisions: Appeal Mechanisms, Evidence, and Outcomes (2025) · cites this paper as Lyons et al. (2021)
Algorithmic contestability remains under-researched, especially from a technical perspective.
“It is widely agreed that algorithmic contestability remains under-researched, especially from a technical perspective (Lyons, Velloso, and Miller 2021;Alfrink et al. 2023;Maxwell and Dumas 2023;Yurrita et al. 2025).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Lyons, Velloso, and Miller 2021
The goals and nature of contestability are multifaceted and often debated, making both its legal and technical implementation challenging.
“The goals and nature of contestability are, however, multifaceted and often debated, which makes both its legal and technical implementation challenging (Lyons, Velloso, and Miller 2021).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Lyons, Velloso, and Miller 2021
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References 96 · Cited by 4
- Authors
- Lyons, H., Velloso, E., & Miller, T.
- Affiliations
- The University of Melbourne (AU)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2021-04-13
- Volume
- 5
- Issue
- CSCW1
- Pages
- 1-25
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W3134155787
- Semantic Scholar
- 7e3d58213b6aff8bd725332bbd5483bf74ecc2f4
- Cited by
- 4
- References
- 96
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Melbourne School of Engineering, University of Melbourne (Ingenium Scholarship)
- Australian Research Council (DE180100315 and DP190103414)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- contestability, algorithmic decision-making, algorithmic accountability, AI ethics, AI ethics framework, high-stakes decisions, individual rights, human decision-making, stakeholder perspectives
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