Risk Assessment for Machine Learning Models

NeurIPS 2020

Risk Assessment for Machine Learning Models

Dec 06, 2020
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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. Speakers: Paul Schwerdtner, Florens Gressner, Felix Assion, Wiebke Gunther, Nikhil Kapoor, René Sass, Fabian Hueger, Peter Schlicht

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