Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review

Background The use of Artificial Intelligence or AI is rising in higher education. With this rise, the morality of AI programs is being questioned. There is, as such, a need to understand how notions of Fairness, Accountability, Transparency, and Ethics or FATE are identified in the AI and higher education studies to date. Purpose This systematic review paper aims to understand definitions and studies on FATE and AI in the higher education literature. The contribution of this work is to provide a summary of FATE development and the synthesis of the challenges and potentials of each of the reviewed studies. Method A total of 33 publications from SCOPUS and Web of Science (WoS) were included in this systematic literature review. We examined definitions of FATE noted in the reviewed articles (may have been multiple in each study) and grouped them into descriptive (understandable by laypeople) and technical (containing jargon) definitions. We also examined the main FATE term studied in detail in each reviewed article and grouped them into qualitative and quantitative studies. Results Findings show more descriptive definitions exist (especially for fairness) and similarly quantitative definitions mostly emerge for Fairness. Findings also show more quantitative studies exist (especially for fairness) and qualitative definitions mostly emerge for ethics. Generally, though, there are more definitions than relevant studies conducted in the literature. Conclusion This systematic literature review offers a summary of definitions and studies conducted for FATE terms and AI in the higher education literature. Future work may benefit from bridging the gap between laypeople and experts by linking descriptive definitions with technical ones as well as qualitative studies with quantitative ones. Moreover, future work can study accountability and transparency further and make the study of FATE terms more longitudinal, open-access, and reproducible.