Contestable Artificial Intelligence

Contestable Artificial Intelligence: Constructive design research for public artificial intelligence systems that are open and responsive to dispute

Alfrink, Kars (TU Delft Internet of Things)

Kortuem, G.W. (promotor)
Doorn, N. (promotor)
Keller, A.I. (copromotor)

Delft University of Technology

2024-05-23

This thesis investigates the use of artificial intelligence (AI) in public policy execution. To contribute to preserving citizen autonomy, it introduces the concept of ‘contestability’—a system quality that ensures citizens retain control over their lives in the face of AI systems and can influence AI system development. The central research aim is to explore sociotechnical design interventions that enhance the contestability of public AI systems.

Utilizing constructive design research, the thesis reports on several studies in which researchers collaborate with design practitioners to create artifacts that function as data generation instruments. Methods encompass interaction design, speculative design, and information design, with case studies in smart electric vehicle charging, urban monitoring camera cars, and fraud risk models, all situated in Amsterdam.

Key findings include varying perceptions of AI transparency between citizens and experts, a design framework for contestable AI, challenges in local government implementation, and the metaphors designers use for public AI.

The research advocates for integrating citizen feedback into AI systems, promoting dialogue between citizens and system controllers, and enhancing democratic involvement in AI development. It also highlights the importance of design in AI implementation, emphasizing speculative design as a method for generating relevant data and guiding ideation and specification processes.

Concluding, the thesis calls for a greater engagement of design researchers and practitioners with political philosophy to understand the democratic implications of their work in AI and related fields.

https://doi.org/10.4233/uuid:0b014ee6-67a8-4c59-8598-4bfd771595a3

978-94-6366-833-0

2026-05-23

Institutional Repository

doctoral thesis

© 2024 Kars Alfrink