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.

通过利用人类认知能力渐进地接收更多细节来提供更详细的解释，通过提供基础+递增因素来帮助用户阅读和记住更准确的解释，从而改善可理解性和可记忆性，并评估Incremental XAI相较于基准解释方法在忠实度、可记忆性和可理解性方面的性能。

增量式可解释性人工智能：逐步解释的可记忆理解