Chaudhry, MA;
Cukurova, M;
Luckin, R;
(2022)
A Transparency Index Framework for AI in Education.
In: Rodrigo, MM and Matsuda, N and Cristea, AI and Dimitrova, V, (eds.)
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022.
(pp. pp. 195-198).
Springer: Cham, Switzerland.
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Abstract
Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.
| Type: | Proceedings paper |
|---|---|
| Title: | A Transparency Index Framework for AI in Education |
| Event: | 23rd International Conference, AIED 2022 |
| ISBN-13: | 9783031116469 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-11647-6_33 |
| Publisher version: | http://dx.doi.org/10.1007/978-3-031-11647-6_33 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media UCL > Provost and Vice Provost Offices > School of Education UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10154952 |
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