Equity Beyond Bias in Language Technologies for Education
Elijah Mayfield, Michael Madaio, Shrimai Prabhumoye, David Gerritsen, Brittany McLaughlin, Ezekiel Dixon-Román, Alan W Black
Abstract
There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, identifying future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.- Anthology ID:
- W19-4446
- Volume:
- Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Helen Yannakoudakis, Ekaterina Kochmar, Claudia Leacock, Nitin Madnani, Ildikó Pilán, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 444–460
- Language:
- URL:
- https://aclanthology.org/W19-4446
- DOI:
- 10.18653/v1/W19-4446
- Cite (ACL):
- Elijah Mayfield, Michael Madaio, Shrimai Prabhumoye, David Gerritsen, Brittany McLaughlin, Ezekiel Dixon-Román, and Alan W Black. 2019. Equity Beyond Bias in Language Technologies for Education. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 444–460, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Equity Beyond Bias in Language Technologies for Education (Mayfield et al., BEA 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W19-4446.pdf