Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute
Not on the map. Why?
Abstract (arXiv)
Local governments increasingly use artificial intelligence (AI) for automated decision-making. Contestability, making systems responsive to dispute, is a way to ensure they respect human rights to autonomy and dignity. We investigate the design of public urban AI systems for contestability through the example of camera cars: human-driven vehicles equipped with image sensors. Applying a provisional framework for contestable AI, we use speculative design to create a concept video of a contestable camera car. Using this concept video, we then conduct semi-structured interviews with 17 civil servants who work with AI employed by a large northwestern European city. The resulting data is analyzed using reflexive thematic analysis to identify the main challenges facing the implementation of contestability in public AI. We describe how civic participation faces issues of representation, public AI systems should integrate with existing democratic practices, and cities must expand capacities for responsible AI development and operation.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 4 of its 86 references that are in the map. First posted on arXiv 9 Feb 2023.
Builds on
4 of 86 references.
References · 4
- 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 66
- 2021A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfarecited by 23
- 2021Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquirycited by 14
- 2013Efficient Data Collection in Multimedia Vehicular Sensing Platformscited by 1
Built on by
Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.
9 papers.
- 2024Contestable AI needs Computational Argumentationcited by 12
- 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AIcited by 5
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Courtcited by 3
- 2026Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberationcited by 3
- 2025Explainable AI Systems Must Be Contestable: Here's How to Make It Happencited by 2
- 2025Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Societycited by 2
- 2025What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparednesscited by 2
- 2025Towards Meaningful Transparency in Civic AI Systemscited by 1
- 2024From Stem to Stern: Contestability Along AI Value Chainscited by 0
- 2026Reflexis: Supporting Reflexivity and Rigor in Collaborative Qualitative Analysis through Design for Deliberation21 Jan 2026
- 2025Towards Meaningful Transparency in Civic AI Systems9 Oct 2025
- 2025What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness26 Sep 2025
- 2025Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society5 Jun 2025
- 2025Explainable AI Systems Must Be Contestable: Here's How to Make It Happen2 Jun 2025
- 2024From Stem to Stern: Contestability Along AI Value Chains2 Aug 2024
- 2024Contestable AI needs Computational Argumentation17 May 2024
- 2024(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court13 Mar 2024
- 2023A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI10 Feb 2023