1 link tagged with all of: bias + policy + fair machine learning
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A new ICML paper argues that shifting fair machine learning toward causal reasoning actually harms policy objectives like diversity and equity, contrary to what researchers expected. The authors show that causal methods can produce worse outcomes than simpler approaches like removing sensitive features entirely.
- Causal fairness frameworks, despite seeming like improvements over traditional methods, can actively damage diversity and equity efforts in practice
- Removing sensitive attributes (the traditional "blinding" approach) sometimes outperforms causal reasoning for achieving policy goals
- The counterintuitive finding challenges the current direction of fair ML research, which has largely embraced causal thinking as more rigorous and principled