Bias and Privacy

In this project, we address both privacy protection and gender bias mitigation in classification models simultaneously. We first introduce a selective privacy-preserving method that obscures individuals’ sensitive information by adding noise to word embeddings. Then, we propose a negative multi-task learning framework to mitigate gender bias, which involves a main task and a gender prediction task.

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