解释效果因人而异，需考虑认知与情境差异。
Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making
- 对比与选择性解释的效果受使用者、场景和任务影响
- 不同解释策略在认知负荷与参与度上表现各异
- 适合个性化设计AI交互界面，避免一刀切
近期可解释人工智能（XAI）研究探讨了AI辅助决策中‘好’解释的定义。尽管对比性和选择性解释被广泛认为更人性化，但现有研究结果不一致。本研究聚焦解释评估的认知维度，评估六种不同对比策略与信息选择性的解释，并深入分析其评价背后的影响因素。结果发现，对比性解释并非普遍最受欢迎或最易理解；不同解释方式的接受程度取决于使用者身份、使用时机、解释内容及认知负荷与社会技术背景。因此，我们呼吁对解释策略采取更细致的视角，为适应个体与情境差异的AI决策界面设计提供启示。
原文摘要 · Abstract (English)
Recent XAI studies have investigated what constitutes a \textit{good} explanation in AI-assisted decision-making. Despite the widely accepted human-friendly properties of explanations, such as contrastive and selective, existing studies have yielded inconsistent findings. To address these gaps, our study focuses on the cognitive dimensions of explanation evaluation, by evaluating six explanations with different contrastive strategies and information selectivity and scrutinizing factors behind their valuation process. Our analysis results find that contrastive explanations are not the most preferable or understandable in general; Rather, different contrastive and selective explanations were appreciated to a different extent based on who they are, when, how, and what to explain -- with different level of cognitive load and engagement and sociotechnical contexts. Given these findings, we call for a nuanced view of explanation strategies, with implications for designing AI interfaces to accommodate individual and contextual differences in AI-assisted decision-making.
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