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Trustworthy Machine Learning
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AI
DOI：10.1109/MIS.2022.3152946.png)
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Abstract
En 中文
Machine learning (ML) techniques have numerous applications inmany fields, including healthcare, medicine, finance, marketing, and cyber security. For example, ML techniques are being applied to determine whether to give a loan to a customer or whether the computing system has been attacked. However, theML techniques themselves may be subject to attacks andmay discriminate when determiningwho should get the loan. Therefore, theMLtechniques have to be secure, ensure privacy of the individuals, incorporate fairness and be accurate. Such collection of ML techniques has come to be known as trustworthy machine learning (trustworthyML). This article describes an architecture to support scalable trustworthyML and describes the features that have to be incorporated into theML techniques to ensure that they are trustworthy.
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