We propose a novel approach to human interaction with artificial intelligence systems (HAII), alternative to the mainstream dyadic one where humans and AI are seen as interacting agents. Through two quantitative experiments and two qualitative in-field case studies, we show that the mainstream HAII paradigm presents potentially harmful design shortcomings as it can trigger negative dynamics such as automation bias and prejudices. Our proposal, on the other hand, is grounded in the Computer-Supported Cooperative Work literature, in which AI can be conceived as a component of a Knowledge Artifact (KA). This consists of an ecosystem of knowledge creation tools whose goal is to support a Ba (after Nonaka), i.e. a collective of competent decision makers. We highlight the cooperative nature of decision making and the AI functionalities that a KA should embed. These include eXplainable AI solutions, aimed at facilitating appropriation, but also functionalities that enable reasoning in a collaborative setting. Finally, we discuss how moving intelligence and agency from individual agents to the human collective can help to mitigate the shortcomings of dyadic HAII (e.g., deskilling), re-distribute responsibility in critical tasks, and revisit the HAII research agenda to align it with the needs of increasingly wide, heterogeneous and complex teams.

Cabitza, F., Campagner, A., Simone, C. (2021). The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 155(November 2021) [10.1016/j.ijhcs.2021.102696].

The need to move away from agential-AI: Empirical investigations, useful concepts and open issues

Cabitza F.;Campagner A.
;
Simone C.
2021

Abstract

We propose a novel approach to human interaction with artificial intelligence systems (HAII), alternative to the mainstream dyadic one where humans and AI are seen as interacting agents. Through two quantitative experiments and two qualitative in-field case studies, we show that the mainstream HAII paradigm presents potentially harmful design shortcomings as it can trigger negative dynamics such as automation bias and prejudices. Our proposal, on the other hand, is grounded in the Computer-Supported Cooperative Work literature, in which AI can be conceived as a component of a Knowledge Artifact (KA). This consists of an ecosystem of knowledge creation tools whose goal is to support a Ba (after Nonaka), i.e. a collective of competent decision makers. We highlight the cooperative nature of decision making and the AI functionalities that a KA should embed. These include eXplainable AI solutions, aimed at facilitating appropriation, but also functionalities that enable reasoning in a collaborative setting. Finally, we discuss how moving intelligence and agency from individual agents to the human collective can help to mitigate the shortcomings of dyadic HAII (e.g., deskilling), re-distribute responsibility in critical tasks, and revisit the HAII research agenda to align it with the needs of increasingly wide, heterogeneous and complex teams.
Articolo in rivista - Articolo scientifico
Artificial intelligence; Ba; Intelligent systems; Knowledge artifact; Machine learning
English
29-lug-2021
2021
155
November 2021
102696
open
Cabitza, F., Campagner, A., Simone, C. (2021). The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 155(November 2021) [10.1016/j.ijhcs.2021.102696].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/324837
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