- R Govys August - Designing Against Bias in Machine Learning and AILink visible for attendees
Speaker: David Corliss
Bias in machine learning algorithms is one of the most important ethical and operational issues in statistical practice today. This presentation describes common sources of bias and how to develop study designs to measure and minimize it. Analysis of disparate impact is used to quantify bias in existing and new applications. Also, a comparison algorithm can be developed that is designed to be fully transparent and without features subject to bias. Comparison to this bias-minimized model can identify areas as bias in other algorithms. These design strategies are described in detail with practical examples in R.
Register here: https://amstat.zoom.us/webinar/register/WN_xJ_1tmv0TUe-SiLr5YIy2Q