Visioning a two-level human-machine communication framework: initiating conversations between explainable AI and communication

Abstract

Amid mounting interest in artificial intelligence (AI) technology, communication scholars have sought to understand humans' perceptions of and attitudes toward AI's predictions, recommendations, and decisions. Meanwhile, scholars in the nascent but growing field of explainable AI (XAI) have aimed to clarify AI's operational mechanisms and make them interpretable, visible, and transparent. In this conceptual article, we suggest that a conversation between human-machine communication (HMC) and XAI is advantageous and necessary. Following the introduction of these two areas, we demonstrate how research on XAI can inform the HMC scholarship regarding the human-in-the-loop approach and the message production explainability. Next, we expound upon how communication scholars' focuses on message sources, receivers, features, and effects can reciprocally benefit XAI research. At its core, this article proposes a two-level HMC framework and posits that bridging the two fields can guide future AI research and development.