Measuring for Biases in AI and ML Applications

Is all data made equal? Learn practical ways of identifying biases in AI and ML applications, starting with where data may fail, such as in computer vision, recommendation systems and natural language processing (NLP) applications. In this session, you gain access to a broad range of case studies—from a startup acquired by Amazon, to a community-focused tech non-profit founded to inspire underrepresented groups to explore AI and other tech fields. You also get an introduction to resources, tools and concrete tips on how to make changes toward bias mitigation in AI and ML systems.

Lead Instructor: Alexander I. Iliev, Ph.D., earned his doctorate from the College of Engineering at the University of Miami (UM) in 2009. He holds two patents in the Digital Audio Watermarking and Data Enhancement fields. Dr. Iliev’s research interests are in the fields of Big Data analytics, signal processing, personalization using speech and image signals, AI, ML and data mining.

Expert Speaker: Nashlie H. Sephus, Ph.D., is an Applied Sciences Manager at Amazon Web Services. The “Tech Evangelist” for Amazon AI, Dr. Sephus focuses on fairness and identifying biases at AWS AI. In 2018, Dr. Sephus became the founder and CEO of The Bean Path, a non-profit organization based in Jackson, Miss., assisting individuals and startups with technical expertise and guidance.