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Thursday, August 22 • 9:30am - 10:15am
Bayesed and Confused: Assessing the Ethical Implications of AI models

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AI models that generate predictions have ethical implications when deployed in the real world. This has led to efforts to combat algorithmic bias and fairness by analyzing the confusion matrix to understand the implications of model misclassification. This presentation showcases a free, publicly available app that helps data scientists assess algorithmic bias. The app is powered by a probabilistic Bayesian model that supports the interpretation of the confusion matrix. The presentation focuses on the use case of predicting rare adverse events in the hospital setting.

Speakers
avatar for Alex Bohl, PhD

Alex Bohl, PhD

Director of Data Science, Mathematica
Dr. Bohl applies machine learning algorithms to healthcare claims to risk-adjust quality measures and design value-based payment programs. He directs Mathematica’s data science staff and leads business development efforts.


Thursday August 22, 2019 9:30am - 10:15am
Room 408 595 Commonwealth Avenue, Boston, MA 02215, USA

Attendees (42)