Human-AI Collaboration: From Explainable AI to Co-Creating Meaning

Authors

Emil Hardarson, Reykjavik UniversityFollow
Frida Ivarsson, Reykjavik UniversityFollow
Anna Sigríður Islind, Reykjavik UniversityFollow
Erna Sif Arnardóttir, Reykjavik UniversityFollow
María Óskarsdóttir, Reykjavik UniversityFollow

Abstract

As AI is more commonly used in decision-making, it has become increasingly important to understand how humans and AI systems can collaborate effectively. One of the aspects of human-AI collaboration is explainable AI (XAI), which aims to make decisions made by AI systems comprehensible to humans. This paper introduces a framing perspective on XAI in human-AI collaboration. With this theory, we propose a shift from persuasion-based XAI, where humans passively receive algorithmic explanations, to co-creation in XAI, where humans and AI collaboratively create meaningful explanations. We motivate this shift through a frame analysis contrasting conventional XAI development with empirical data from views on XAI in sleep medicine. The paper contributes new insights for enhancing human-AI collaboration through a more interactive and contextually aware approach to XAI.

Recommended Citation

Hardarson, Emil; Ivarsson, Frida; Islind, Anna Sigríður; Arnardóttir, Erna Sif; and Óskarsdóttir, María, "Human-AI Collaboration: From Explainable AI to Co-Creating Meaning" (2024). ACIS 2024 Proceedings. 148.
https://aisel.aisnet.org/acis2024/148