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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.

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1009240
Subject
Variables;
Machine learning;
Algorithms;
Data analysis;
Datasets;
Hypothesis testing;
Regression analysis;
Coronaviruses;
COVID-19;
Classification;
Causality;
Statistical prediction
Business indexing term
Identifier / keyword
Machine learning; Responsibility; Machine learning; Causality; Interpretability; Data science; Predictive statistics; Inference; Causality
Title
Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning
Author
Marmolejo-Ramos, Fernando 1   VIAFID ORCID Logo  ; Ospina, Raydonal 2 ; García-Ceja, Enrique 3 ; Correa, Juan C. 4 

1  University of South Australia, Centre for Change and Complexity in Learning, Adelaide, Australia (GRID:grid.1026.5) (ISNI:0000 0000 8994 5086) 
2  Universidade Federal de Pernambuco, CASTLab, Department of Statistics, Recife, Brazil (GRID:grid.411227.3) (ISNI:0000 0001 0670 7996) 
3  Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico (GRID:grid.419886.a) (ISNI:0000 0001 2203 4701) 
4  CESA Business School, Bogotá, Bogotá, DC, Colombia (GRID:grid.441875.b) (ISNI:0000 0004 0486 0518) 
Publication title
Journal of Statistical Theory and Applications; Melbourne
Volume
21
Issue
4
Pages
175-185
Publication year
2022
Publication date
Dec 2022
Publisher
Springer Nature B.V.
Place of publication
Melbourne
Country of publication
Netherlands
Publication subject
Mathematics, Statistics
ISSN
15387887
e-ISSN
22141766
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2022-09-15
Milestone dates
2022-09-03 (Registration); 2022-07-19 (Received); 2022-09-02 (Accepted)
Publication history
 
 
   First posting date
15 Sep 2022
DOI
https://doi.org/10.1007/s44199-022-00048-y
ProQuest document ID
2746830839
Document URL
https://www.proquest.com/scholarly-journals/ingredients-responsible-machine-learning/docview/2746830839/se-2?accountid=208611
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2023-11-28
Database
2 databases
  • Coronavirus Research Database
  • ProQuest One Academic