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Charleston Data Science Meetup

Bias in Machine Learning Applications

Understanding bias, auditing and quality control are key challenges for developing machine learning methods in healthcare. Models that have been trained on biased data, have the potential to automate decisions that are unfair and inequitable. This talk discusses strategies and methods for domain and outcome bias exploration to evaluate performance equitably across subgroups and whether the training data are representative of novel data expected in the model application.