探究NLP中可解释性与隐私保护的兼容性，发现二者可共存。
When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
- 用差分隐私和后验可解释性方法实证研究两者关系。
- 发现任务类型、文本隐私化与解释方法影响二者权衡。
- 提出实用建议，指导未来可信NLP系统设计。
在值得信赖的自然语言处理研究中，可解释性和隐私保护是两个重要方向。尽管近年来对可解释性和隐私保护的NLP研究兴趣显著增加，但二者交叉领域的研究仍显不足，导致我们对能否同时实现可解释性与隐私保护的理解存在较大空白。本文基于差分隐私（Differential Privacy, DP）和后验可解释性（Post-hoc Explainability）的主流方法，对NLP中的隐私-可解释性权衡进行了实证研究。研究揭示了二者之间复杂的相互关系，该关系受下游任务性质、文本隐私化方法及可解释性方法选择等多种因素影响。研究指出，隐私与可解释性并非必然冲突，二者在特定条件下可共存。本文总结了一系列面向未来交叉研究的实践建议。
原文摘要 · Abstract (English)
In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of explainability and privacy. While research interest in both explainable and privacy-preserving NLP has increased considerably in recent years, there remains a lack of investigation at the intersection of the two. This leaves a considerable gap in understanding of whether achieving both explainability and privacy is possible, or whether the two are at odds with each other. In this work, we conduct an empirical investigation into the privacy-explainability trade-off in the context of NLP, guided by the popular overarching methods of Differential Privacy (DP) and Post-hoc Explainability. Our findings include a view into the intricate relationship between privacy and explainability, which is formed by a number of factors, including the nature of the downstream task and choice of the text privatization and explainability method. In this, we highlight the potential for privacy and explainability to co-exist, and we summarize our findings in a collection of practical recommendations for future work at this important intersection.
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