Suggested Readings

Works selected to build a common understanding on the main topics of the workshop.

Novel Design Principles for Cognitive Engagement

• Frischmann, B., & Selinger, E. (2018). Re-engineering humanity. Cambridge University Press.

• Cabitza, F., Natali, C., Famiglini, L., Campagner, A., Caccavella, V., & Gallazzi, E. (2024). Never tell me the odds: Investigating pro-hoc explanations in medical decision making. Artificial Intelligence in Medicine, 102819.

• Robbins, S. (2025). What machines shouldn’t do. AI & SOCIETY, 1-12.

• Advait Sarkar. 2024. AI Should Challenge, Not Obey. Commun. ACM 67, 10 (October 2024), 18–21. https://doi.org/10.1145/3649404

• Natali, C. (2023). Per Aspera ad Astra, or Flourishing via Friction: Stimulating Cognitive Activation by Design through Frictional Decision Support Systems. In CEUR WORKSHOP PROCEEDINGS (Vol. 3481, pp. 15-19).

• Miller, T. (2023). Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 333-342).

• Cabitza, F., & Natali, C. (2022). Open, multiple, adjunct. Decision support at the time of relational AI. In HHAI2022: Augmenting Human Intellect (pp. 243-245). IOS Press.

• Cabitza, F. (2021). Cobra AI: Exploring Some Unintended Consequences. Machines We Trust: Perspectives on Dependable AI, 87.

• Inman, S., & Ribes, D. (2019). "Beautiful Seams" Strategic Revelations and Concealments. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-14).

• Cox, A. L., Gould, S. J., Cecchinato, M. E., Iacovides, I., & Renfree, I. (2016). Design frictions for mindful interactions: The case for microboundaries. In Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems (pp. 1389-1397).

• Grosse-Hering, B., Mason, J., Aliakseyeu, D., Bakker, C., & Desmet, P. (2013). Slow design for meaningful interactions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3431-3440).

Measurement and Mitigation Strategies for Over-reliance and Under-reliance: 

• S. de Jong, V. Paananen, B. Tag, N. van Berkel, "Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial Explanations", Proceedings of the ACM on Human-Computer Interaction – CSCW, 2025.

• Cabitza, F., Campagner, A., Angius, R., Natali, C., Reverberi, C. (2023). AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making. In CHI ’23: the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, April 23–28, 2023, Hamburg, Germany.

• Lee, H. P. H., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers.

• Bertrand, A., Belloum, R., Eagan, J. R., & Maxwell, W. (2022). How cognitive biases affect XAI-assisted decision-making: A systematic review. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (pp. 78-91).

• C. Rastogi, et al., Deciding fast and slow: The role of cognitive biases in ai-assisted decision-making, Proceedings of the ACM on Human-Computer Interaction 6 (2022) 1–22. 

• Kliegr, T., Bahník, Š., & Fürnkranz, J. (2021). A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Artificial Intelligence, 295, 103458.

• Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-21.

Calibration of Appropriate Trust in AI

• Schemmer, M., Kuehl, N., Benz, C., Bartos, A., & Satzger, G. (2023). Appropriate reliance on AI advice: Conceptualization and the effect of explanations. In Proceedings of the 28th International Conference on Intelligent User Interfaces (pp. 410-422)

• Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., ... & Weld, D. (2021). Does the whole exceed its parts? the effect of AI explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-16). 

• Cabitza, F., Campagner, A., Ronzio, L., Cameli, M., Mandoli, G. E., Pastore, M. C., ... & Gamboa, H. (2023). Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis. Artificial Intelligence in Medicine, 138, 102506.

• Cabitza, F., Campagner, A., Natali, C., Parimbelli, E., Ronzio, L., & Cameli, M. (2023). Painting the black box white: experimental findings from applying XAI to an ECG reading setting. Machine Learning and Knowledge Extraction, 5(1), 269-286.

Governance solutions for Cognitively Engaging AI Design

• Frischmann, Brett & Moshe Y. Vardi, Better Digital Contracts with Prosocial Friction-in-Design, 65 Jurimetrics J. (2025), at https://ssrn.com/abstract=4918003

• Frischmann, B. M., & Benesch, S. (2022). Friction-In-Design Regulation as 21St Century Time, Place and Manner Restriction. Place and Manner Restriction. 

• Frischmann, B., & Ohm, P. (2023). Governance Seams. Harvard Journal of Law & Technology, 37(3).

Applications and Case Studies

• Cabitza, F., Natali, C., Famiglini, L., Campagner, A., Caccavella, V., & Gallazzi, E. (2024). Never tell me the odds: Investigating pro-hoc explanations in medical decision making. Artificial Intelligence in Medicine, 102819.

• Drosos, I., Sarkar, A., & Toronto, N. (2025). " It makes you think": Provocations Help Restore Critical Thinking to AI-Assisted Knowledge Work. arXiv preprint arXiv:2501.17247. 

• Cabitza, F., Campagner, A., Famiglini, L., Natali, C., Caccavella, V., & Gallazzi, E. (2023). Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice. In International Cross-Domain Conference for Machine Learning and Knowledge Extraction (pp. 155-169). Cham: Springer Nature Switzerland. 

• Bach, A. K. P., Nørgaard, T. M., Brok, J. C., & van Berkel, N. (2023, April). “If I had all the time in the world”: Ophthalmologists’ perceptions of anchoring bias mitigation in clinical AI support. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-14).

• Mejtoft, T., Parsjö, E., Norberg, O., & Söderström, U. (2023, March). Design Friction and Digital Nudging: Impact on the Human Decision-Making Process. In Proceedings of the 2023 5th International Conference on Image, Video and Signal Processing (pp. 183-190). 

• Dell’Acqua, F. (2022). Falling asleep at the wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters. Working paper.  

• Krzysztof Z. Gajos and Lena Mamykina. Do people engage cognitively with ai? impact of ai assistance on incidental learning. IUI '22, page 794–806, New York, NY, USA, 2022. Association for Computing Machinery.

• Park, J. S., Barber, R., Kirlik, A., & Karahalios, K. (2019). A slow algorithm improves users' assessments of the algorithm's accuracy. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-15.