📢 New preprint!
“When majority rules, minority loses: bias amplification of gradient descent”
We often blame biased data but training also amplifies biases. Our paper explores how ML algorithms favor stereotypes at the expense of minority groups.
➡️ arxiv.org/abs/2505.13122
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arxiv.org
When majority rules, minority loses: bias amplification of gradient descent
Despite growing empirical evidence of bias amplification in machine learning, its theoretical foundations remain poorly understood. We develop a formal framework for majority-minority learning tasks, ...
