Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem to find the largest deviation of a supervised learning model from a reference model regarded as safe. We then show how interpretability facilitates this safety assessment. For models including decision trees, generalized linear and additive models, the maximum deviation can be computed exactly and efficiently. For tree ensembles, which are not regarded as interpretable, discrete optimization techniques can still provide informative bounds. For a broader class of piecewise Lipschitz functions, we leverage the multi-armed bandit literature to show that interpretability produces tighter (regret) bounds on the maximum deviation. We present case studies, including one on mortgage approval, to illustrate our methods and the insights about models that may be obtained from deviation maximization.
翻译:可解释和可解释的机器学习最近出现了兴趣的激增。 我们把安全作为激增背后的一个关键动机,并使可解释性和安全之间的关系更加量化。 为了评估安全性,我们通过优化问题引入了最大偏差的概念,以找到受监督学习模式与被视为安全的参考模式的最大偏差。然后我们展示了可解释性如何促进安全评估。对于包括决策树在内的模型、普遍的线性模型和添加型模型,最大偏差可以精确和有效地计算。对于不被视为可解释性的树群,离散优化技术仍然可以提供信息界限。对于更广泛的小类的精密利普施奇茨功能,我们利用多臂带文学来表明可解释性会更紧密(gret)限制最大偏差。我们提出案例研究,包括抵押批准,以说明我们的方法和从偏离最大化中可能获得的模型的洞察力。