A new fair-ML method learns interpretable differences between observed and desired human decisions to reduce downstream outcome disparity, with a proof of full mitigation under simplifying assumptions.
Fairness in machine learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Learning Representational Disparities
A new fair-ML method learns interpretable differences between observed and desired human decisions to reduce downstream outcome disparity, with a proof of full mitigation under simplifying assumptions.