Fairness and Equity in AI for Education

Dr. Alan Rubel provides examples of implications of AI systems for fairness in both education and the criminal justice system. He compares human bias with AI bias, but also talks about the dangers of administrators offloading decisions to AI. Dr. Alan Rubel is an associate professor and director of the Center For Law, Society & Justice at the Information School of the University of

Dr. Beverly Woolf talks about the importance of obtaining a sample of students when training AI models that is representative of the population the system is intended to serve. She also talks about how AI can be useful to teachers and the importance of involving the teachers in the AI development process. Dr. Beverly Woolf is a research professor of computer science and the Directo

Dr. Kyle Jones talks about the importance of giving students choice on what data is collected about them for use in learning analytics. He also talks about responsibilities of institutions of higher education about justifying their use of student data. He also emphasizes bringing voices from diverse backgrounds into the development process of learning analytics products. Dr. Kyle J

Dr. Rene Kizilcec describes many sources of unfair outcomes of AI in education. He also describes the difficulties of transferability of AI models across contexts, and the fairness concerns that come not from the structure of AI but from how it is used in K12 schools and institutions of higher education. Dr. Rene Kizilcec is an Assistant Professor of Information Science and Directo

Dr. Michael Madaio talks about how solutions to ensure fairness in AI are both technical and social and provides several examples of directions of research on fairness in AI. He points out the limitations of transparency in AI systems for education as a means of ensuring fairness. Dr. Michael Madaio is a post-doctoral researcher at Microsoft Research. He has a PhD in Human-Computer