Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics

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The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of machine learning (ML) models amplifying societal biases. In this paper, we propose adapting income inequality metrics from economics to complement existing model-level fairness metrics, which foc...

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Detalles Bibliográficos
Autores principales: Lazovich, Tomo, Belli, Luca, Gonzales, Aaron, Bower, Amanda, Tantipongpipat, Uthaipon, Lum, Kristian, Huszár, Ferenc, Chowdhury, Rumman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403369/
https://www.ncbi.nlm.nih.gov/pubmed/36033598
http://dx.doi.org/10.1016/j.patter.2022.100568
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