Open Access Research article

Testing and Trusting Machine Learning Systems

Sajjan Shiva1* and Deepak Venugopal1

1Department of Computer Science, The University of Memphis, USA

Corresponding Author

Received Date:January 26, 2021;  Published Date: February 24, 2020


Machine learning systems are now all over the place. These systems provide predictions in a black box mode masking their internal logic from the user. This absence of explanation creates practical and ethical issues. The explanation of a prediction reduces relying on black-box traditional ML classifiers. Trustable Artificial Intelligence is the current area of interest. Testing of such systems has also not been formalized. We highlight these two issues in this paper.

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