The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-Making

Human-AI collaborative decision-making can achieve better outcomes than either party individually. The success of this collaboration can depend on whether the human decision-maker perceives the AI contribution as beneficial to the decision-making process. Beneficial AI explanations are often describ...

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Bibliographic Details
Published in:Proceedings of the ACM on human-computer interaction 2024-11, Vol.8 (CSCW2), p.1-37, Article 517
Main Authors: Ferguson, Sharon, Aoyagui, Paula Akemi, Rizvi, Rimsha, Kim, Young-Ho, Kuzminykh, Anastasia
Format: Article
Language:English
Subjects:
Artificial intelligence
Computing methodologies
Empirical studies in HCI
Human computer interaction (HCI)
Human-centered computing
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Summary:Human-AI collaborative decision-making can achieve better outcomes than either party individually. The success of this collaboration can depend on whether the human decision-maker perceives the AI contribution as beneficial to the decision-making process. Beneficial AI explanations are often described as relevant, convincing, and trustworthy. Yet, we know little about the characteristics of explanations that result in these perceptions. Focusing on collaborative subjective decision-making, using the context of subtle sexism, where explanations can surface new interpretations, we conducted a user study (N=20) to explore the structural and content characteristics that affect perceptions of human and AI-generated verbal (text and audio) explanations. We find four groups of characteristics (Tone, Grammatical Elements, Argumentative Sophistication and Relation to User), and that the effect of these characteristics on the perception of explanations for subtle sexism depends on the perceived author. Thus, we also identify which explanation characteristics participants use to identify the author of an explanation. Demonstrating the relationship between these characteristics and explanation perceptions, we present a categorized set of characteristics that system builders can leverage to produce the appropriate perception of an explanation for various sensitive contexts. We also highlight human perception biases and associated issues resulting from these perceptions.
ISSN:2573-0142
2573-0142
DOI:10.1145/3687056