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Bayesian Strategic Classification
We initiate the study of partial information release by the learner in strategic classification.
On the Vulnerability of Fairness Constrained Learning to Malicious Noise
For example, for Demographic Parity we show we can incur only a $\Theta(\alpha)$ loss in accuracy, where $\alpha$ is the malicious noise rate, matching the best possible even without fairness constraints.
Agnostic Multi-Robust Learning Using ERM
A fundamental problem in robust learning is asymmetry: a learner needs to correctly classify every one of exponentially-many perturbations that an adversary might make to a test-time natural example.
Sequential Strategic Screening
We initiate the study of strategic behavior in screening processes with multiple classifiers.
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline
Even if the firm is required to interview all of those who pass the final round, the tests themselves could have the property that qualified individuals from some groups pass more easily than qualified individuals from others.
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
Multiple fairness constraints have been proposed in the literature, motivated by a range of concerns about how demographic groups might be treated unfairly by machine learning classifiers.
