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Counterfactual fairness (CANCELLED)

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If you have a question about this talk, please contact Qingyuan Zhao.

Structural causal models are powerful tools for understanding algorithmic fairness. Strong causal assumptions can be more interpretable and enable transparent deliberation over algorithm design choices. This talk illustrates an approach through examples including defining fairness for predictive algorithms, fair policy optimization, and intersectional fairness. While these examples focus on fairness, causal modeling can be applied in similar ways toward achieving other values or objectives in responsible machine learning or data-driven decisions broadly.

This talk is part of the Statistics series.

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