Abstract
In AI, the algorithm is not coded but produced by a combination of training data, labelling (concepts) and the neural network. This is the essence of machine learning. The algorithm is not directly insightful and cannot be bug-fixed directly: it is “black box development”.
AI systems are used in contexts with diverse data and usage. Choice in training data and labels brings risks in bias and transparency with possible high impact on real people. Testing AI focusses on these risks. An AI tester needs moral, social and worldly intelligence and awareness to bring out the users, their expectations and translate these in test cases that can be run repetitively and automated. AI testing includes setting up metrics that translate test results in a meaningful and quantifiable evaluation of the system in order for developers to optimize the system.
Chapter PDF
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Numan, G. (2020). Testing Artificial Intelligence. In: Goericke, S. (eds) The Future of Software Quality Assurance. Springer, Cham. https://doi.org/10.1007/978-3-030-29509-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-29509-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29508-0
Online ISBN: 978-3-030-29509-7
eBook Packages: Computer ScienceComputer Science (R0)