mlsanityData to 14 Oct 2026

Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions

arXiv 2103.01774 ↗First posted 23 Feb 2021cs.CYcited by 18

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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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  2. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  3. 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platformcited by 21
  4. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  5. 2023User Experience Design Professionals' Perceptions of Generative Artificial Intelligencecited by 16
  6. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  7. 2022Towards a multi-stakeholder value-based assessment framework for algorithmic systemscited by 12
  8. 2024Time Can Invalidate Algorithmic Recoursecited by 8
  9. 2023Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalismcited by 7
  10. 2025Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matchingcited by 4
  11. 2022Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infantscited by 1
  12. 2026From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Makingcited by 1
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  2. 2026MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identificationcited by 1
  3. 2024From Stem to Stern: Contestability Along AI Value Chainscited by 0
  4. 2024Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectivescited by 0
  5. 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisionscited by 0
  6. 2026Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employmentcited by 0