HIGHLIGHTS
- who: Algorithmic Components et al. from the University of, Australia have published the article: What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components, in the Journal: (JOURNAL) of 09/04/2022
- how: This paper introduces a collection of hands-on training materials - slides video recordings and Jupyter Notebooks - that provide guidance through the process of building and evaluating bespoke modular surrogate explainers for tabular data. The training resources described by this paper introduce a novel learning paradigm for algorithmic explainability of data-driven predictive systems based on artificial_intelligence and . . .
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