Join us for networking starting at 5:30 pm followed by a program starting at 6 pm, featuring Dr. James Foulds of UMBC. His talk:
Differential Fairness for Machine Learning and Artificial Intelligence Systems: Unbiased Decisions with Biased Data
With the rising influence of machine learning in our daily lives, there are growing concerns that biases can lead algorithms to discriminate. Biased data can produce biased outcomes along lines of gender, race, sexual orientation, and political ties, with important real-world consequences. Thus, there is an urgent need for machine learning algorithms that make unbiased decisions with biased data.
We propose a novel framework for measuring and correcting bias in data-driven algorithms, with inspiration from privacy-preserving machine learning and Bayesian probabilistic modeling. A case study on census data demonstrates the utility of our approach.
Dr. James Foulds is an Assistant Professor in the Department of Information Systems at UMBC. His research interests are in both applied and foundational machine learning, focusing on probabilistic latent variable models and the inference algorithms to learn them from data.