The work of the EDSAFE centers around the SAFE Benchmarks Framework as we engage stakeholders to align equitable outcomes for all learners and improved working experiences for dedicated and innovative educators. We intend to clarify the urgency and specific areas of need to prevent failures in data management that compromise the potential for how responsible AI can be a lever for equity and innovation while protecting student privacy. Frameworks and benchmarks are important to innovation as a means of targeted guidance, focusing disparate efforts towards shared objectives and outcomes and ensuring the development of appropriate guidelines and guardrails.
S. Warburton, and Y. Mor. EuroPLoP'22: 27th European Conference on Pattern Languages of Programs, New York, NY, United States, Association for Computing Machinery, (2022)
E. Hakami, and D. Hernandez-Leo. LAK21: 11th International Learning Analytics and Knowledge Conference, page 269–279. New York, NY, USA, Association for Computing Machinery, (2021)