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Found 2 papers, 1 papers with code
Fairness and bias correction in machine learning for depression prediction: results from four study populations
Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations.
Candidate Labeling for Crowd Learning
In this paper, the use of candidate labeling for crowd learning is proposed, where the annotators may provide more than a single label per instance to try not to miss the real label.
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