TY - JOUR
T1 - Ingredients for Responsible Machine Learning
T2 - 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.
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.
KW - Causality
KW - Inference
KW - Machine learning
KW - Predictive statistics
UR - http://www.scopus.com/inward/record.url?scp=85138259812&partnerID=8YFLogxK
U2 - 10.1007/s44199-022-00048-y
DO - 10.1007/s44199-022-00048-y
M3 - Review article
AN - SCOPUS:85138259812
SN - 2214-1766
VL - 21
SP - 175
EP - 185
JO - Journal of Statistical Theory and Applications
JF - Journal of Statistical Theory and Applications
IS - 4
ER -