论文标题
安全模型解释的利润率
Margin-distancing for safe model explanation
论文作者
论文摘要
在结果环境中，机器学习模型的日益增长强调了透明度和对游戏脆弱性之间的重要且看似不可调和的张力。尽管这在法律文献中引发了巨大的辩论，但对这一论点的技术研究相对较少。在这项工作中，我们提出了这种紧张的清洁表述，以及一种使透明度和游戏之间的权衡的方法。我们将游戏的来源确定为靠近模型的\ emph {决策边界}的点。我们开始研究如何提供基于示例的解释，这些解释是扩展的，但与边界点标签相对于不确定的版本空间一致。最后，我们通过对现实世界数据集这一权衡的实证研究来提供理论结果。
The growing use of machine learning models in consequential settings has highlighted an important and seemingly irreconcilable tension between transparency and vulnerability to gaming. While this has sparked sizable debate in legal literature, there has been comparatively less technical study of this contention. In this work, we propose a clean-cut formulation of this tension and a way to make the tradeoff between transparency and gaming. We identify the source of gaming as being points close to the \emph{decision boundary} of the model. And we initiate an investigation on how to provide example-based explanations that are expansive and yet consistent with a version space that is sufficiently uncertain with respect to the boundary points' labels. Finally, we furnish our theoretical results with empirical investigations of this tradeoff on real-world datasets.
