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 |
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| Outros autores: | , , |
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Springer Nature B.V.
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| Acceso en liña: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Marmolejo-Ramos, Fernando |u University of South Australia, Centre for Change and Complexity in Learning, Adelaide, Australia (GRID:grid.1026.5) (ISNI:0000 0000 8994 5086) | |
| 245 | 1 | |a Ingredients for Responsible Machine Learning: A Commented Review of <i>The Hitchhiker’s Guide to Responsible Machine Learning</i> | |
| 260 | |b Springer Nature B.V. |c Dec 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Variables | ||
| 653 | |a Machine learning | ||
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| 653 | |a Classification | ||
| 653 | |a Causality | ||
| 653 | |a Statistical prediction | ||
| 700 | 1 | |a Ospina, Raydonal |u Universidade Federal de Pernambuco, CASTLab, Department of Statistics, Recife, Brazil (GRID:grid.411227.3) (ISNI:0000 0001 0670 7996) | |
| 700 | 1 | |a García-Ceja, Enrique |u Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico (GRID:grid.419886.a) (ISNI:0000 0001 2203 4701) | |
| 700 | 1 | |a Correa, Juan C. |u CESA Business School, Bogotá, Bogotá, DC, Colombia (GRID:grid.441875.b) (ISNI:0000 0004 0486 0518) | |
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