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PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity.
Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
We find statistically significant differences in recommender performance by both age and gender.
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