Achieving Fair Inference Using Error-Prone Outcomes

Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels...

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Detalles Bibliográficos

Autores principales:	Boeschoten, Laura, Jan van Kesteren, Erik, Bagheri, Ayoub, Oberski, Daniel L.
Formato:	Artículo
Lenguaje:	inglés
Publicado:	2021
Materias:	Algorithmic Bias Fair Machine Learning Latent Variable Model Measurement Error Measurement Invariance
Acceso en línea:	https://dialnet.unirioja.es/servlet/oaiart?codigo=9032584
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