[论文解读] Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Sandra Wachter, Brent Mittelstadt+1 more|arXiv (Cornell University)|Nov 1, 2017

Privacy-Preserving Technologies in Data被引用 162

一句话总结

本文主张数据控制者可以在不暴露算法决策系统的全部内部运作的情况下，为受影响的个人提供有用的反事实解释，聚焦于可操作的指导，以质疑或获得期望的结果。

ABSTRACT

There has been much discussion of the right to explanation in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the black box of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR. We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the closest possible world, without needing to explain the internal logic of the system.

研究动机与目标

• 在GDPR下阐明可解释性的必要性并识别打开算法决策黑箱的障碍。
• 提出无条件的反事实解释，作为一种实用的解释形式。
• 从个人角度界定解释的三个目标：理解、质疑和改变结果。

提出的方法

• 分析解释如何在不暴露内部模型细节的情况下帮助数据主体。
• 主张无条件的反事实解释，描述为实现期望结果所需的最小世界变更。
• 将解释目标映射到GDPR条款，并评估对每个目标的法律支持。

实验结果

研究问题

• RQ1 反事实解释是否能够在不披露内部模型逻辑的情况下让数据主体了解作出决定的原因？
• RQ2 反事实解释是否支持在GDPR框架下的质疑权利和获得更期望结果的权利？
• RQ3 哪种类型的解释最符合GDPR，以在保护专有系统的同时赋予个人权力？

主要发现

• 反事实解释可以在不暴露黑箱的情况下向个人提供信息。
• 无条件的反事实解释支持理解、可质疑性以及获得期望结果的追索权。
• 此类解释通过关注所需的最小变更而非内部机制来符合GDPR的目标。