In this paper, we consider some key characteristics that AI should exhibit to enable hybrid agencies that include subject-matter experts and their AI-enabled decision aids. We will hint at the design requirements of guaranteeing that AI tools are: open, multiple, continuous, cautious, vague, analogical and, most importantly, adjunct with respect to decision-making practices. We will argue that especially adjunction is an important condition to design for. Adjunction entails the design and evaluation of human-AI interaction protocols aimed at improving AI usability, while also guaranteeing user satisfaction and human and social sustainability. It does so by boosting people's cognitive motivation for interacting analytically with the outputs, reducing overreliance on AI and improving performance.

Cabitza, F., Natali, C. (2022). Open, Multiple, Adjunct. Decision Support at the Time of Relational AI. In S. Schlobach, M. Pérez-Ortiz, M. Tielman (a cura di), HHAI2022: Augmenting Human Intellect (pp. 243-245). IOS press [10.3233/FAIA220204].

Open, Multiple, Adjunct. Decision Support at the Time of Relational AI

Cabitza, F;Natali, C
2022

Abstract

In this paper, we consider some key characteristics that AI should exhibit to enable hybrid agencies that include subject-matter experts and their AI-enabled decision aids. We will hint at the design requirements of guaranteeing that AI tools are: open, multiple, continuous, cautious, vague, analogical and, most importantly, adjunct with respect to decision-making practices. We will argue that especially adjunction is an important condition to design for. Adjunction entails the design and evaluation of human-AI interaction protocols aimed at improving AI usability, while also guaranteeing user satisfaction and human and social sustainability. It does so by boosting people's cognitive motivation for interacting analytically with the outputs, reducing overreliance on AI and improving performance.
Capitolo o saggio
Decision support; Interaction protocols; Machine Learning; Relational Artificial Intelligence; Usability;
Italian
HHAI2022: Augmenting Human Intellect
Schlobach, S; Pérez-Ortiz, M; Tielman, M
2022
978-1-64368-308-9
354
IOS press
243
245
Cabitza, F., Natali, C. (2022). Open, Multiple, Adjunct. Decision Support at the Time of Relational AI. In S. Schlobach, M. Pérez-Ortiz, M. Tielman (a cura di), HHAI2022: Augmenting Human Intellect (pp. 243-245). IOS press [10.3233/FAIA220204].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/416377
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