Three Paradoxes to Reconcile to Promote Safe, Fair, and Trustworthy AI in Education
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3657604.3664658.pdf
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738.38 KB
Format
Adobe PDF
Checksum (MD5)
1023ddfa4079053a19c94cc3417d75bf
Author(s)
Slama, Rachel
Toutziaridi, Amalia Christina
Reich, Justin
Date Issued
July 9, 2024
Publisher
ACM|Proceedings of the Eleventh ACM Conference on Learning @ Scale
Citation
Slama, Rachel, Toutziaridi, Amalia Christina and Reich, Justin. 2024. "Three Paradoxes to Reconcile to Promote Safe, Fair, and Trustworthy AI in Education."
Version
Final published version
Abstract
Incorporating recordings of teacher-student conversations into the training of LLMs has the potential to improve AI tools. Although AI developers are encouraged to put "humans in the loop" of their AI safety protocols, educators do not typically drive the data collection or design and development processes underpinning new technologies. To gather insight into privacy concerns, the adequacy of safety procedures, and potential benefits of recording and aggregating data at scale to inform more intelligent tutors, we interviewed a pilot sample of teachers and administrators using a scenario-based, semi-structured interview protocol. Our preliminary findings reveal three "paradoxes" for the field to resolve to promote safe, fair, and trustworthy AI. We conclude with recommendations for education stakeholders to reconcile these paradoxes and advance the science of learning.
Description
L@S '24, July 18–20, 2024, Atlanta, GA, USA
MIT Department
Massachusetts Institute of Technology. Program in Comparative Media Studies/Writing
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3657604.3664658
