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Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

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Publicado en:Journal of Statistical Theory and Applications vol. 21, no. 4 (Dec 2022), p. 175
Autor principal: Marmolejo-Ramos, Fernando
Otros Autores: Ospina, Raydonal, García-Ceja, Enrique, Correa, Juan C.
Publicado:
Springer Nature B.V.
Materias:
Variables
Machine learning
Algorithms
Data analysis
Datasets
Hypothesis testing
Regression analysis
Coronaviruses
COVID-19
Classification
Causality
Statistical prediction
Acceso en línea:Citation/Abstract
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Resumen:In The hitchhiker’s guide to responsible machine learning, Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ’s book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.
ISSN:1538-7887
2214-1766
DOI:10.1007/s44199-022-00048-y
Fuente:Computer Science Database