2 papers
cs.LG2020
Null-sampling for Interpretable and Fair Representations
Thomas Kehrenberg, Myles Bartlett, Oliver Thomas +1
We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant co…
cs.LG2018
Discovering Fair Representations in the Data Domain
Novi Quadrianto, Viktoriia Sharmanska, Oliver Thomas
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…