Interaction Design for Explainable AI: Workshop Proceedings

TL;DR

This paper discusses the importance of human-centered interaction design in explainable AI, emphasizing the need for user-focused explanations to improve trust and ethical decision-making.

Contribution

It highlights the necessity of integrating human-computer interaction principles into XAI and encourages discourse on designing better interaction paradigms for explainability.

Findings

Identifies gaps in current XAI research regarding human interaction

Proposes a focus on human-centered design in XAI development

Stimulates discussion on interaction challenges in explainability

Abstract

As artificial intelligence (AI) systems become increasingly complex and ubiquitous, these systems will be responsible for making decisions that directly affect individuals and society as a whole. Such decisions will need to be justified due to ethical concerns as well as trust, but achieving this has become difficult due to the `black-box' nature many AI models have adopted. Explainable AI (XAI) can potentially address this problem by explaining its actions, decisions and behaviours of the system to users. However, much research in XAI is done in a vacuum using only the researchers' intuition of what constitutes a `good' explanation while ignoring the interaction and the human aspect. This workshop invites researchers in the HCI community and related fields to have a discourse about human-centred approaches to XAI rooted in interaction and to shed light and spark discussion on interaction design challenges in XAI.