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

Fernando Marmolejo-Ramos; Raydonal Ospina; Enrique García-Ceja; Juan C Correa

doi:10.1007/s44199-022-00048-y

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

J Stat Theory Appl. 2022;21(4):175-185. doi: 10.1007/s44199-022-00048-y. Epub 2022 Sep 15.

Authors

Fernando Marmolejo-Ramos¹, Raydonal Ospina², Enrique García-Ceja³, Juan C Correa⁴

Affiliations

¹ Centre for Change and Complexity in Learning, University of South Australia, Adelaide, SA 5001 Australia.
² CASTLab, Department of Statistics, Universidade Federal de Pernambuco, Recife, Pernambuco 51280-000 Brazil.
³ Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, 64849 Monterrey, Nuevo León Mexico.
⁴ CESA Business School, Bogotá, Bogotá, DC, 110231 Colombia.

Abstract

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.

Keywords: Causality; Inference; Machine learning; Predictive statistics.

Publication types

Review