# Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions

arXiv: https://arxiv.org/abs/2103.01774 · HTML: https://mlsanity.com/paper/2103.01774/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2021.

## Built on by (most cited first, 18 of 18)

- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023)
- [VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform](https://mlsanity.com/paper/2504.14904/) (2025)
- [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Time Can Invalidate Algorithmic Recourse](https://mlsanity.com/paper/2410.08007/) (2024)
- [Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism](https://mlsanity.com/paper/2311.06864/) (2023)
- [Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching](https://mlsanity.com/paper/2512.03553/) (2025)
- [Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants](https://mlsanity.com/paper/2212.08821/) (2022)
- [From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making](https://mlsanity.com/paper/2603.18895/) (2026)
- [Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research](https://mlsanity.com/paper/2604.17843/) (2026)
- [MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification](https://mlsanity.com/paper/2606.14786/) (2026)
- [From Stem to Stern: Contestability Along AI Value Chains](https://mlsanity.com/paper/2408.01051/) (2024)
- [Transforming disaster risk reduction with AI and big data: Legal and interdisciplinary perspectives](https://mlsanity.com/paper/2410.07123/) (2024)
- [What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions](https://mlsanity.com/paper/2505.06428/) (2025)
- [Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment](https://mlsanity.com/paper/2606.07543/) (2026)

## 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.