Abstract
While the previous chapter covered Human-in-the-Loop and its importance in building responsible AI algorithms, it’s paramount to ensure the transparency and explainability of ML models following HITL processes. This chapter reviews “explainability,” also known as XAI and its implementation.
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© 2023 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature
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Duke, T. (2023). Explainability. In: Building Responsible AI Algorithms. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9306-5_7
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DOI: https://doi.org/10.1007/978-1-4842-9306-5_7
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-9305-8
Online ISBN: 978-1-4842-9306-5
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