Detection and evaluation of bias-inducing features in machine learning

The cause-to-effect analysis can help us decompose all the likely causes of a problem, such as an undesirable business situation or unintended harm to the individual(s). This implies that we can identify how the problems are inherited, rank the causes to help prioritize fixes, simplify a complex pro...

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Veröffentlicht in:	Empirical software engineering : an international journal Jg. 29; H. 1; S. 22
Hauptverfasser:	Openja, Moses, Laberge, Gabriel, Khomh, Foutse
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.02.2024 Springer Nature B.V Springer Verlag
Schlagworte:	Bias Compilers Computer Science Context Decision making Evaluation Interpreters Machine learning Programming Languages Software Engineering/Programming and Operating Systems Special Issue on Equitable Data and Technology Subject specialists Bias Sensitive features Fairness Machine learning
ISSN:	1382-3256, 1573-7616
Online-Zugang:	Volltext
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