Building Fair AI: Practical Guide to Ethical Algorithms

Emma Pierson serves as a distinguished instructor at Fred Hutchinson Cancer Center and is a computer scientist at Cornell University, where she specializes in using AI and data science to address health disparities across demographic groups. Her academic credentials include a bachelor's in physics and master's in computer science from Stanford, a master's in statistics from Oxford as a Rhodes Scholar, and a doctorate in computer science from Stanford. As the instructor for "Practical Steps for Building Fair AI Algorithms," she brings her expertise in developing ethical AI solutions that address systemic inequalities in healthcare and social systems. Her groundbreaking research includes analyzing COVID-19 transmission patterns, racial disparities in traffic stops, and women's health data across 109 countries. Beyond academia, she contributes to major publications like The New York Times and The Atlantic, making complex data science concepts accessible to broader audiences. Her work has directly influenced policy changes, including the Los Angeles Police Department's revision of their stop practices and state health departments' COVID-19 reopening strategies

Kowe Kadoma serves as an instructor at Fred Hutchinson Cancer Center, specializing in ethical AI implementation and algorithmic fairness. As an instructor for "Practical Steps for Building Fair AI Algorithms," she focuses on teaching professionals how to develop and implement AI systems that promote equity and fairness across diverse populations. Her course curriculum emphasizes practical approaches to understanding and addressing bias in AI algorithms, covering crucial topics such as ethical data collection, model training principles, and healthcare algorithm design. Through her teaching, she helps students master the complexities of algorithmic fairness definitions, transparency in AI systems, and the challenging ethical dilemmas that arise in AI implementation. Her approach combines theoretical understanding with hands-on application, ensuring students gain practical skills in designing and implementing fair AI solutions while considering their broader societal impact.