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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| Materias: | |
| 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 |