Deep learning's preponderance across scientific domains has reshaped high-stakes decision-making, making it essential to follow rigorous operational frameworks that include both Right-to-Privacy (RTP) and Right-to-Explanation (RTE). This paper examines the complexities of combining these two requirements. For RTP, we focus on `Differential privacy' (DP), which is considered the current \textit{gold standard} for privacy-preserving machine learning due to its strong quantitative guarantee of privacy. For RTE, we focus on post-hoc explainers: they are the \textit{go-to} option for model auditing as they operate independently of model training. We formally investigate DP models and various commonly-used post-hoc explainers: how to evaluate these explainers subject to RTP, and analyze the intrinsic interactions between DP models and these explainers. Furthermore, our work throws light on how RTP and RTE can be effectively combined in high-stakes applications. Our study concludes by outlining an industrial software pipeline, with the example of a wildly used use-case, that respects both RTP and RTE requirements.

本研究探讨了深度学习在高风险决策中的隐私权（RTP）与可解释权（RTE）之间的复杂性，重点关注差分隐私（DP）及后处理解释器的结合方法。研究表明，通过系统性评估DP模型与后处理解释器的相互作用，能够在高风险应用中有效地融合这两种需求，提供了一种实用的软件开发流程作为示例。

两个命令的故事：隐私与可解释性