In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on human judgment and decisions. We distinguish between a negative component of dominance (automation bias) and a positive one (algorithm appreciation) by focusing on and systematizing the patterns of interaction between human judgment and AI support, or reliance patterns, and their associated cognitive effects. We then define statistical approaches for measuring these dimensions of dominance, as well as corresponding qualitative visualizations. By reporting about four medical case studies, we illustrate how the proposed methods can be used to inform assessments of dominance and of related cognitive biases in real-world settings. Our study lays the groundwork for future investigations into the effects of introducing AI support into naturalistic and collaborative decision-making.

Cabitza, F., Campagner, A., Angius, R., Natali, C., Reverberi, F. (2023). AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making. In A. Schmidt, K. Väänänen (a cura di), CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-20). Association for Computing Machinery, New York, NY, United States [10.1145/3544548.3581095].

AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making

Cabitza, Federico
Co-primo
;
Campagner, Andrea
Co-primo
;
Angius, Riccardo;Natali, Chiara;Reverberi, Franco
Ultimo
2023

Abstract

In this article, we propose a conceptual and methodological framework for measuring the impact of the introduction of AI systems in decision settings, based on the concept of technological dominance, i.e. the influence that an AI system can exert on human judgment and decisions. We distinguish between a negative component of dominance (automation bias) and a positive one (algorithm appreciation) by focusing on and systematizing the patterns of interaction between human judgment and AI support, or reliance patterns, and their associated cognitive effects. We then define statistical approaches for measuring these dimensions of dominance, as well as corresponding qualitative visualizations. By reporting about four medical case studies, we illustrate how the proposed methods can be used to inform assessments of dominance and of related cognitive biases in real-world settings. Our study lays the groundwork for future investigations into the effects of introducing AI support into naturalistic and collaborative decision-making.
Capitolo o saggio
Human AI interaction; technological dominance; Human AI collaboration
English
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Schmidt, A; Väänänen, K
2023
9781450394215
Association for Computing Machinery, New York, NY, United States
1
20
354
Cabitza, F., Campagner, A., Angius, R., Natali, C., Reverberi, F. (2023). AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making. In A. Schmidt, K. Väänänen (a cura di), CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-20). Association for Computing Machinery, New York, NY, United States [10.1145/3544548.3581095].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/416357
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