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Mitigating Bias in ML Models for Depression by sskshirsagar1711 is a document available to read on EtoBox.

What is Mitigating Bias in ML Models for Depression about?

The study investigates methods to mitigate socio-demographic bias in machine learning models for estimating depression severity using language-based features. It involves feature extraction, gender and race classification, and depression severity estimation, with a focus on bias mitigation through feature removal and fairness techniques. Results indicate that Random Forest models consistently outperformed Deep Learning models, and removing demographic features improved fairness and performance, particularly

Author

sskshirsagar1711

Language

EN