当可解释性遇上隐私：自然语言处理语境下事后可解释性与差分隐私交叉点的研究
When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
AI总结：
本文针对自然语言处理中可解释性与隐私的交叉点缺乏研究的问题，基于差分隐私和事后可解释性方法实证调查了隐私-可解释性权衡，揭示了两者间复杂关系，并提出了共存潜力及未来研究的实用建议。
摘要：
在可信自然语言处理（NLP）的研究中，出现了许多重要的研究领域，包括可解释性和隐私。尽管近年来对可解释和隐私保护NLP的研究兴趣大幅增加，但在两者的交叉点上仍然缺乏研究。这使得在理解是否能够同时实现可解释性和隐私，或者两者是否相互矛盾方面，存在相当大的空白。在这项工作中，在差分隐私（DP）和事后可解释性这两种流行的总体方法的指导下，我们对NLP语境下的隐私-可解释性权衡进行了实证调查。我们的发现包括对隐私与可解释性之间复杂关系的深入观察，这种关系由多种因素形成，包括下游任务的性质以及文本隐私化和可解释性方法的选择。在此，我们强调了隐私与可解释性共存的潜力，并将我们的发现总结为一系列实用建议，以供未来在这一重要交叉点的研究参考。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
