Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
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Abstract (arXiv)
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.
Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2021.
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Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 3; 2026: 4.
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- 2023User Experience Design Professionals' Perceptions of Generative Artificial Intelligence26 Sep 2023
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- 2022Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants17 Dec 2022
- 2022Seamful XAI: Operationalizing Seamful Design in Explainable AI12 Nov 2022
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- 2022Interactive Model Cards: A Human-Centered Approach to Model Documentation5 May 2022
- 2022Interactive Model Cards: A Human-Centered Approach to Model Documentationcited by 61
- 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
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- 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
- 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systemscited by 12
- 2024Time Can Invalidate Algorithmic Recoursecited by 8
- 2023Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalismcited by 7
- 2025Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matchingcited by 4
- 2022Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infantscited by 1
- 2026From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Makingcited by 1
12 most cited of 17 All 17 (JSON)
- 2026Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment1 May 2026
- 2026Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research20 Apr 2026
- 2026From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making19 Mar 2026
- 2025Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching3 Dec 2025
- 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions9 May 2025
- 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform21 Apr 2025
- 2024Time Can Invalidate Algorithmic Recourse10 Oct 2024
- 2024Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives20 Sep 2024
- 2024From Stem to Stern: Contestability Along AI Value Chains2 Aug 2024
- 2023Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism12 Nov 2023
- 2023A Critical Survey on Fairness Benefits of Explainable AI15 Oct 2023
- 2023User Experience Design Professionals' Perceptions of Generative Artificial Intelligence26 Sep 2023
12 newest of 17 All 17 (JSON)