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Found 6 papers, 3 papers with code
When Fair Classification Meets Noisy Protected Attributes
Our study provides insights into the practical implications of using fair classification algorithms in scenarios where protected attributes are noisy or partially available.
Subverting Fair Image Search with Generative Adversarial Perturbations
In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the ranking model behave unfairly without having access to the model or training data?
Subscriptions and external links help drive resentful users to alternative and extremist YouTube videos
Do online platforms facilitate the consumption of potentially harmful content?
Structurizing Misinformation Stories via Rationalizing Fact-Checks
Misinformation has recently become a well-documented matter of public concern.
FairCanary: Rapid Continuous Explainable Fairness
We incorporate QDD into a continuous model monitoring system, called FairCanary, that reuses existing explanations computed for each individual prediction to quickly compute explanations for the QDD bias metrics.
When Fair Ranking Meets Uncertain Inference
Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm.
