Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies Proceedings of a Workshop (2019) / Chapter Skim
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1 Introduction
1 Introduction
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Workshop speakers and participants discussed research challenges related to the following topics: Critical analysis of the current state of machine learning and artificial intelligence (AI) algorithms and systems that are used to generate analytic products from disparate structured and unstructured data types and to detect anomalies; Statistical methods that can be used to evaluate confidence hierarchies, model uncertainty, and error propagation, and manage risk as a function of time and complexity; Approaches for ensuring that machine-generated products compare favorably with those of trained human analysts; and Techniques for responding to adversarial manipulation of input data to influence analytical products by exploiting weaknesses in machine learning and AI algorithms and vulnerabilities in their implementation.
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The views contained in this proceedings are those of the individual workshop participants and do not necessarily represent the views of the participants as a whole, the planning committee, or the National Academies of Sciences, Engineering, and Medicine.
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