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Towards Explainability in Machine Learning: The Formal Methods Way | IEEE Journals & Magazine | IEEE Xplore

Towards Explainability in Machine Learning: The Formal Methods Way


Abstract:

Classification is a central discipline of machine learning (ML) and classifiers have become increasingly popular to support or replace human decisions. We encounter them ...Show More

Abstract:

Classification is a central discipline of machine learning (ML) and classifiers have become increasingly popular to support or replace human decisions. We encounter them as email spam detectors, as decision support systems, for example in healthcare, as aid in interpreting X-rays in breast cancer detection, or in the financial and insurance sector, for financial and risk analysis. For example, Facebook uses classifiers to predict the likelihood that users will navigate or click in a certain way, at scale, for millions and millions of users every day. They also play a significant role in various areas of computer vision, where traffic signals and other objects need to be identified in order to “read” a situation during assisted or autonomous driving.
Published in: IT Professional ( Volume: 22, Issue: 4, 01 July-Aug. 2020)
Page(s): 8 - 12
Date of Publication: 17 July 2020

ISSN Information:


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