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Discovering Fair Representations in the Data Domain
[article]
2019
arXiv
pre-print
Interpretability and fairness are critical in computer vision and machine learning applications, in particular when dealing with human outcomes, e.g. inviting or not inviting for a job interview based on application materials that may include photographs. One promising direction to achieve fairness is by learning data representations that remove the semantics of protected characteristics, and are therefore able to mitigate unfair outcomes. All available models however learn latent embeddings
arXiv:1810.06755v2
fatcat:nbttkxm4hnekvjy46gt7jfwzfa