Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
This conceptual paper aims to explore judgment in the context of automated decision-making systems (ADS). To achieve this, we adopt a modern version of Aristotle’s notion of phronesis to understand judgment. We delineate seven elements of judgment which provide insights into what humans are better at, and what AI is better at in relation to automated decision-making. These elements are sources of knowledge that guide action including not-knowing, emotions, sensory perception, experience, intuition, episteme, and techne. Our analysis suggests that most of these attributes are not transferable to AI systems, because judgment in human decision-making requires the integration of all which involves considering the contextual and affective resources of phronesis, and the competence to make value judgments. The paper contributes to unpack human judgment capacities and what needs to be cultivated to achieve ‘good’ AI systems that serves humanity as well as guiding future information systems researchers to explore human-AI judgment further.
Recommended Citation
Koutsikouri, Dina; Hylving, Lena; Lindberg, Susanne; and Bornemark, Jonna, "Seven Elements of Phronesis: A Framework for Understanding Judgment in Relation to Automated Decision-Making" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 7.
https://aisel.aisnet.org/hicss-56/os/ai_and_organizing/7
Seven Elements of Phronesis: A Framework for Understanding Judgment in Relation to Automated Decision-Making
Online
This conceptual paper aims to explore judgment in the context of automated decision-making systems (ADS). To achieve this, we adopt a modern version of Aristotle’s notion of phronesis to understand judgment. We delineate seven elements of judgment which provide insights into what humans are better at, and what AI is better at in relation to automated decision-making. These elements are sources of knowledge that guide action including not-knowing, emotions, sensory perception, experience, intuition, episteme, and techne. Our analysis suggests that most of these attributes are not transferable to AI systems, because judgment in human decision-making requires the integration of all which involves considering the contextual and affective resources of phronesis, and the competence to make value judgments. The paper contributes to unpack human judgment capacities and what needs to be cultivated to achieve ‘good’ AI systems that serves humanity as well as guiding future information systems researchers to explore human-AI judgment further.
https://aisel.aisnet.org/hicss-56/os/ai_and_organizing/7