论文标题
机器学习模型的风险评估
Risk Assessment for Machine Learning Models
论文作者
论文摘要
在本文中，我们提出了一个框架，用于评估与在指定环境中部署机器学习模型相关的风险。为此，我们将风险定义从决策理论到机器学习。我们开发和实施一种方法，该方法允许定义部署方案，在每种情况下指定的条件下测试机器学习模型，并估算与正在测试的机器学习模型的输出相关的损坏。使用每种情况的可能性以及我们定义机器学习模型的\ emph {关键风险指标}的估计损害。 场景的定义和通过其可能性加权的定义允许在整个应用的多个应用领域进行机器学习中的标准化风险评估。特别是，在我们的框架中，可以评估机器学习模型对随机输入损坏的鲁棒性，由于环境变化而引起的分配变化以及可以评估对抗性扰动。
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment. For that we carry over the risk definition from decision theory to machine learning. We develop and implement a method that allows to define deployment scenarios, test the machine learning model under the conditions specified in each scenario, and estimate the damage associated with the output of the machine learning model under test. Using the likelihood of each scenario together with the estimated damage we define \emph{key risk indicators} of a machine learning model. The definition of scenarios and weighting by their likelihood allows for standardized risk assessment in machine learning throughout multiple domains of application. In particular, in our framework, the robustness of a machine learning model to random input corruptions, distributional shifts caused by a changing environment, and adversarial perturbations can be assessed.
