Mediating Community-AI Interaction through Situated Explanation: The Case of AI-Led Moderation

TL;DR

This paper explores how community members collaboratively understand and explain AI-led moderation decisions, emphasizing the importance of situated explanations rooted in community norms and practices.

Contribution

It introduces a theoretical framework combining XAI and activity theory to analyze community-mediated explanations of AI decisions in online moderation.

Findings

Community members develop collective explanations for AI decisions

Situated explanations are influenced by community norms and shared values

The framework bridges XAI, HCI, and CSCW for community-AI interaction

Abstract

Artificial intelligence (AI) has become prevalent in our everyday technologies and impacts both individuals and communities. The explainable AI (XAI) scholarship has explored the philosophical nature of explanation and technical explanations, which are usually driven by experts in lab settings and can be challenging for laypersons to understand. In addition, existing XAI research tends to focus on the individual level. Little is known about how people understand and explain AI-led decisions in the community context. Drawing from XAI and activity theory, a foundational HCI theory, we theorize how explanation is situated in a community's shared values, norms, knowledge, and practices, and how situated explanation mediates community-AI interaction. We then present a case study of AI-led moderation, where community members collectively develop explanations of AI-led decisions, most of which are automated punishments. Lastly, we discuss the implications of this framework at the intersection of CSCW, HCI, and XAI.