mlsanityData to 7 Oct 2026

Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute

arXiv 2302.04603 ↗First posted 9 Feb 2023cs.HC cs.AI cs.CYcited by 9

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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

  1. 2018Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Makingcited by 66
  2. 2021A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfarecited by 23
  3. 2021Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquirycited by 14
  4. 2013Efficient Data Collection in Multimedia Vehicular Sensing Platformscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.

9 papers.