These are works we have used in our research. In particular these all capture actionable elements that contribute to the contestability of AI systems, either on the level of system features, or development practices.

  1. Aler Tubella, A., Theodorou, A., Dignum, V., & Michael, L. (2020). Contestable Black Boxes. In V. Gutiérrez-Basulto, T. Kliegr, A. Soylu, M. Giese, & D. Roman (Eds.), Rules and Reasoning (Vol. 12173, pp. 159–167). Springer International Publishing.
  2. Almada, M. (2019). Human intervention in automated decision-making: Toward the construction of contestable systems. Proceedings of the 17th International Conference on Artificial Intelligence and Law, ICAIL 2019, 2–11.
  3. Bayamlıoğlu, E. (2021). The right to contest automated decisions under the General Data Protection Regulation: Beyond the so-called “right to explanation.” Regulation and Governance.
  4. Brkan, M. (2019). Do algorithms rule the world? Algorithmic decision-making and data protection in the framework of the GDPR and beyond. International Journal of Law and Information Technology, 27(2), 91–121.
  5. Crawford, K. (2016). Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics. Science, Technology, & Human Values, 41(1), 77–92.
  6. Edwards, L., & Veale, M. (2018). Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”? IEEE Security & Privacy, 16(3), 46–54.
  7. Elkin-Koren, N. (2020). Contesting algorithms: Restoring the public interest in content filtering by artificial intelligence. Big Data & Society, 7(2), 205395172093229.
  8. Henin, C., & Le Métayer, D. (2021). Beyond explainability: Justifiability and contestability of algorithmic decision systems. AI & SOCIETY.
  9. Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E. Z. E., & Atkins, D. C. D. C. (2017). Designing contestability: Interaction design, machine learning, and mental health. DIS 2017 – Proceedings of the 2017 ACM Conference on Designing Interactive Systems, 95–99.
  10. Jewell, M. (2018). Contesting the decision: Living in (and living with) the smart city. International Review of Law, Computers and Technology.
  11. Kariotis, T., & J. Mir, D. (2020). Fighting Back Algocracy: The need for new participatory approaches to technology assessment. Proceedings of the 16th Participatory Design Conference 2020 – Participation(s) Otherwise – Volume 2, 148–153.
  12. König, P. D., & Wenzelburger, G. (2021). The legitimacy gap of algorithmic decision-making in the public sector: Why it arises and how to address it. Technology in Society, 67, 101688.
  13. Lyons, H., Velloso, E., & Miller, T. (2021). Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–25.
  14. Ploug, T., & Holm, S. (2020). The four dimensions of contestable AI diagnostics—A patient-centric approach to explainable AI. Artificial Intelligence in Medicine, 107, 101901.
  15. Sarra, C. (2020). Put Dialectics into the Machine: Protection against Automatic-decision-making through a Deeper Understanding of Contestability by Design. Global Jurist, 20(3), 20200003.
  16. Vaccaro, K., Karahalios, K., Mulligan, D. K., Kluttz, D., & Hirsch, T. (2019). Contestability in Algorithmic Systems. Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing, 523–527.
  17. Vaccaro, K., Sandvig, C., & Karahalios, K. (2020). “At the End of the Day Facebook Does What It Wants”: How Users Experience Contesting Algorithmic Content Moderation. Proceedings of the ACM on Human-Computer Interaction, 4.
  18. Vaccaro, K., Xiao, Z., Hamilton, K., & Karahalios, K. (2021). Contestability For Content Moderation. Proceedings of the ACM on Human-Computer Interaction, 5, 1–28.
  19. Walmsley, J. (2021). Artificial intelligence and the value of transparency. AI & SOCIETY, 36(2), 585–595.