Discovering Fair Representations in the Data Domain

Understand

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 which comes at the cost of being uninterpretable.