The Missing Teacher in AI: Involving Teachers in Metadesign of AI to Ensure FAIRness

Considerations of algorithmic fairness in education are motivated by the growing number of
students who are affected by algorithmic systems. A fair algorithm should not discriminate
against individuals based on their membership in protected groups. However, what it means for
an algorithm to discriminate and definitions of protected groups vary across applications and
situations. Based on previous work on algorithmic fairness, we argue for an increased focus on
situated fairness. This perspective addresses the difficulties and complexities of achieving
fairness in algorithms and allows for teachers to compensate to ensure fairness in their practices.
In this three-year project, we aim to develop new understanding of how to increase the agency
of teachers concerning the design and use of artificial intelligence systems in education (AIEd)
to ensure fairness. We will include secondary school teachers in formative design interventions
to help them understand the risks of bias undermining the beneficial outcomes expected of
AIEd. Our study will be focused on the potential impact of adaptive AI-based teaching
materials. This type of application is intended to serve as a comprehensive resource for a course
and integrates an intelligent tutoring system. Drawing upon activity theory and metadesign,
formative interventions are seen as part of a learning process including both participatory
analyses of the issues raised by the use of AIEd, and the design of solutions to adapt AI to
accommodate local needs. This developmental approach is expected to facilitate the agency of
teachers.

The main outcomes of this project include contributions to the design of formative interventions
for teachers and of guidelines for a situation-sensitive approach to fairness, which will take into
account contextual and individual situations and specific technical configurations.