Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protec...

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Detalles Bibliográficos
Autores principales: Galhotra, Sainyam, Shanmugam, Karthikeyan, Sattigeri, Prasanna, Varshney, Kush R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699829/
https://www.ncbi.nlm.nih.gov/pubmed/34945877
http://dx.doi.org/10.3390/e23121571

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699829/
https://www.ncbi.nlm.nih.gov/pubmed/34945877
http://dx.doi.org/10.3390/e23121571

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