risk prediction

Wednesday, December 5, 2018 - 10:30am - 11:30am
Hamsa Bastani (Wharton School of the University of Pennsylvania)
Machine learning is increasingly used to inform consequential decisions. Yet, these predictive models have been found to exhibit unexpected defects when trained on real-world observational data, which are plagued with confounders and biases. Thus, it is critical to involve domain experts in an interactive process of developing predictive models; interpretability offers a promising way to facilitate this interaction.
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