TY - JOUR
T1 - Ingredients for Responsible Machine Learning: A commented Review of the Hitchhiker’s Guide to Responsible Machine Learning
AU - Marmolejo-Ramos, Fernando
AU - Ospina, Raydonal
AU - García-Ceja, Enrique
AU - Correa, Juan C
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85138259812&partnerID=8YFLogxK
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U2 - 10.1007/s44199-022-00048-y
DO - 10.1007/s44199-022-00048-y
M3 - Review article
C2 - 36160758
SN - 1538-7887
VL - 21
SP - 175
EP - 185
JO - Journal of Statistical Theory and Applications
JF - Journal of Statistical Theory and Applications
IS - 4
ER -