The increasing impact of machine learning and algorithmic decision making on education has brought about growing opportunities and concerns. Evidence has shown that these technologies can perpetuate and even magnify existing educational and social inequities. Research on fair machine learning has aimed to develop algorithms that can detect and, in some cases correct, bias, but this effort within the educational data mining community is still limited. In this workshop, we hope to spur discussion around algorithmic fairness and bias detection as specifically applied in an educational context. Submissions and panels will be invited to discuss (a) collection and preparation of benchmark datasets for bias detection and correction tasks, (b) evaluation protocol definition and metric formulation appropriate for bias and fairness in educational tasks, and (c) countermeasure design and development for biased and unfair circumstances. These specific topics will be complemented by a more general discussion of the education-specific challenges for fair machine learning in education, bringing together perspectives from both industry and academia. This workshop builds on the FATED workshop held at EDM 2020, and we expect the workshop to make connections among already interested researchers and provide a ground for those who want to engage in this area.