Contestability in Algorithmic Systems
Open accessAbstract
As algorithmic (and particularly machine learning) decision making systems become both more widespread and make more important decisions, there are growing concerns about their embedded values and ability to establish legitimacy among decision subjects. We argue that designing for contestability in these systems can assist in surfacing values, aligning system design and use with context, and building legitimacy. However, designing for contestability can be challenging, particularly in systems that are designed to be opaque: systems need to accurately surface embedded values, expose decision making processes in ways that are meaningful for users, support engagement with and allow influence over system performance, and so on. In addition to these technical aspects, designing for contestability may by challenged by the need to protect intellectual property and prevent gaming of the system. In this workshop, we will address goals, audiences, and designs for contestability in algorithmic systems. We hope to develop a taxonomy of contestable systems and understand the value provided by contestability, while bringing together a community to work on this multidisciplinary problem.
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Contestability is a form of procedural justice, a way of giving voice to decision subjects, which increases perceptions of fairness, in particular for marginalized or disempowered populations.
“Contestability is a form of procedural justice, a way of giving voice to decision subjects, which increases perceptions of fairness, in particular for marginalized or disempowered populations.”
From Contestable AI by Design: Towards a Framework (2022) · cites this paper as Vaccaro et al. (2019)
The Ethical Foundations section describes contestability as a form of procedural justice and links having one’s voice heard to perceptions of fairness, with particularly strong effects for marginalized or disempowered populations. But it also says current designs for contestation do not always improve perceived fairness, so the claim’s unqualified assertion of an increase overstates the source. No page number is provided in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredContestability is a core ethical requirement in algorithmic decision-making and a central element in several worldwide legislations.
“Contestability is a core ethical requirement in algorithmic decision-making and, consequently, a central element in several worldwide legislations (Vaccaro et al. 2019;Maxwell and Dumas 2023).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Vaccaro et al. 2019
The Abstract argues that designing for contestability can help build legitimacy in algorithmic systems, and Ethical Foundations links it to fairness and procedural justice. Those passages support its ethical importance, but do not establish it as a core requirement. The paper mentions legal mandates and regulators without saying contestability is a central element of legislation worldwide. (LLM, conf=0.86)
Experimental — accuracy not yet measuredContestability can surface values, align design practice with context of use, and increase the perceived legitimacy of AI systems.
“Vaccaro et al. (2019) argue that contestability can surface values, align design practice with context of use, and increase the perceived legitimacy of AI systems.”
From Contestable AI by Design: Towards a Framework (2022) · cites this paper as Vaccaro et al. (2019)
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- Authors
- Vaccaro, K., Karahalios, K., Mulligan, D. K., Kluttz, D., & Hirsch, T.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- University of California, Berkeley (US)
- Northeastern University (US)
- Venue
- Companion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing
- Publisher
- ACM
- Published
- 2019-11-09
- Pages
- 523-527
- OpenAlex
- W2983930363
- Cited by
- 54
- References
- 11
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 9 · 2023: 6 · 2022: 6 · 2021: 6 · 2020: 4
- FWCI
- 4.18
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Computer Supported Cooperative Work (CSCW) (publishedVersion)
- Funders
- National Science Foundation (1564041)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- contestability, algorithmic decision-making, machine learning systems, embedded value, legitimacy, transparency, explainability, opacity, user engagement, intellectual property
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