Many explainable AI (XAI) techniques strive for interpretability by providing concise salient information, such as sparse linear factors. However, users either only see inaccurate global explanations, or highly-varying local explanations. We propose to provide more detailed explanations by leveraging the human cognitive capacity to accumulate knowledge by incrementally receiving more details. Focusing on linear factor explanations (factors $\times$ values = outcome), we introduce Incremental XAI to automatically partition explanations for general and atypical instances by providing Base + Incremental factors to help users read and remember more faithful explanations. Memorability is improved by reusing base factors and reducing the number of factors shown in atypical cases. In modeling, formative, and summative user studies, we evaluated the faithfulness, memorability and understandability of Incremental XAI against baseline explanation methods. This work contributes towards more usable explanation that users can better ingrain to facilitate intuitive engagement with AI.

翻译：许多可解释人工智能技术通过提供简洁的显著信息（例如稀疏线性因子）来追求可解释性。然而，用户要么只能看到不准确的全局解释，要么面对高度变化局部的解释。我们提出通过利用人类逐步接收更多细节时积累知识的认知能力，来提供更详细的解释。聚焦于线性因子解释（因子×值=结果），我们引入增量可解释人工智能，通过提供基础因子+增量因子，自动为一般实例与异常实例划分解释，帮助用户阅读并记忆更可信的解释。通过重复使用基础因子并减少异常情况下显示的因子数量，可记忆性得到提升。在建模性、形成性与总结性用户研究中，我们评估了增量可解释人工智能相较于基线解释方法的可信度、可记忆性与可理解性。本工作致力于提供更易用的解释，帮助用户更好地内化知识，以促进与人工智能的直观互动。