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Found 7 papers, 2 papers with code
Monotone Individual Fairness
We then study an online classification setting where label feedback is available for positively-predicted individuals only, and present an oracle-efficient algorithm achieving an upper bound frontier of $(\mathcal{O}(T^{2/3+2b}),\mathcal{O}(T^{5/6-b}))$ for regret, number of fairness violations, for $0\leq b \leq 1/6$.
Individually Fair Learning with One-Sided Feedback
We consider an online learning problem with one-sided feedback, in which the learner is able to observe the true label only for positively predicted instances.
Information Discrepancy in Strategic Learning
We initiate the study of the effects of non-transparency in decision rules on individuals' ability to improve in strategic learning settings.
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them.
Metric-Free Individual Fairness in Online Learning
We study an online learning problem subject to the constraint of individual fairness, which requires that similar individuals are treated similarly.
Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative.
Penalizing Unfairness in Binary Classification
We present a new approach for mitigating unfairness in learned classifiers.
