Group study of selected topics in Computer Sciences, usually relating to new developments.

AI is eating the world. Unfortunately, these machine learning models have drawn unfortunate attention in recent years for being “biased” (e.g ttps://fairmlbook.org/) and automating discrimination. Within genomics, a field replete with complicated ML, these biases lead to dramatically different health outcomes for persons of different socioeconomic status or ancestry. Neither AI nor genomics can be fully “de-biased” in a single semester, but we hope to provide a concise yet detailed overview of existing problems and known frameworks to detect and prevent bias through the emerging field of algorithmic fairness. This student-led discussion group (with multiple anticipated guest speakers!) is designed to equip the next generation of researchers with the awareness and structural competencies to address bias, and is divided into two consecutive parts. Part 1: Algorithmic Fairness in AI (approx. weeks 1-6, 8) Part 2: Fairness and Model Portability in Genomics (approx. weeks 7, 9-15) At the end of the course, we hope participants will be able to answer the following in their own terms: 1) What is fairness in machine learning, and what are common causes of unfairness? 2) How can cultural biases impact datasets and modeling assumptions? What kind of approaches can account for this situation? 3) (2-hour option) What parallels exist between algorithmic fairness and model portability in genomics? 4) (2-hour option) How can we account for patterns of genetic variation when analyzing genomic data?

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