Human-Centered Machine Learning

Our April meetup features a presentation, Human-Centered Machine Learning, by Patrick Hall of H2O.ai. After a half-hour of networking and refreshments courtesy of meetup sponsor Allegis Group, our program starts at 6:30 pm. Patrick's presentation illustrates how to combine innovations from several sub-disciplines of machine learning research to train understandable, observationally fair, trustable, and accurate predictive modeling systems. Techniques from research into fair models, directly interpretable Bayesian or constrained machine learning models, and post-hoc explanations can be used to train transparent, observationally fair, and accurate models. Additional techniques from fairness research can be used to check for disparate impact in model predictions and to preprocess data and post-process predictions to ensure the demographic parity of predictive models.