Research article
Testing and Trusting Machine Learning Systems
Sajjan Shiva1* and Deepak Venugopal1
1Department of Computer Science, The University of Memphis, USA
Sajjan Shiva, Memphis, Tennessee
Received Date:January 26, 2021; Published Date: February 24, 2020
Abstract
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.
-
Sajjan Shiva, Deepak Venugopa. Testing and Trusting Machine Learning Systems. On Journ of Robotics & Autom. 1(1): 2021. OJRAT. MS.ID.000503.
-
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.