What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

Research output: Other contribution

Abstract

Publication of a paper describing the training materials in The Journal of Open Source Education.

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title = "What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components",

abstract = "Publication of a paper describing the training materials in The Journal of Open Source Education.",

author = "Kacper Sokol and Alexander Hepburn and Raul Santos-Rodriguez and Peter Flach",

year = "2022",

month = oct,

day = "20",

doi = "10.5281/zenodo.7229153",

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T1 - What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

AU - Sokol, Kacper

AU - Hepburn, Alexander

AU - Santos-Rodriguez, Raul

AU - Flach, Peter

PY - 2022/10/20

Y1 - 2022/10/20

N2 - Publication of a paper describing the training materials in The Journal of Open Source Education.

AB - Publication of a paper describing the training materials in The Journal of Open Source Education.

U2 - 10.5281/zenodo.7229153

DO - 10.5281/zenodo.7229153

M3 - Other contribution

ER -