Equity Beyond Bias in Language Technologies for Education
By Elijah Mayfield, Michael A. Madaio, Shrimai Prabhumoye, David B. Gerritsen, B. McLaughlin, Ezekiel Dixon-Román, Alan W. Black. 2019
46 citations recorded by the source corpus.
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.
Subfields: Natural Language Processing, Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Ruslan Salakhutdinov
- Hal Daumé Iii
- Shrimai Prabhumoye
- Su Lin Blodgett
- Yejin Choi
- Alan W. Black
- Ryan Shaun Joazeiro de Baker
- Aaron Hawn
In Safety, Robustness & Interpretability
- Su Lin Blodgett
- Hal Daumé Iii
- Ruslan Salakhutdinov
- Hanna M. Wallach
- Shrimai Prabhumoye
- Ryan Shaun Joazeiro de Baker
- Alan W. Black
- Aaron Hawn
Citations in the corpus
- Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “For example, Mayfeld et al. (2019) and Ruane et al.”
- Case Study: Deontological Ethics in NLP (2020, background): “…et al., 2019; Schnoebelen, 2017; Schmaltz, 2018), data handling practices (Lewis et al., 2017; Mieskes, 2017) and specific domains like education (Mayfield et al., 2019; Loukina et al., 2019), health care (Šuster et al., 2017; Benton et al., 2017) and conversational agents (Cercas Curry and…”
- Algorithmic Bias in Education (2021, background): “As noted in several articles (i.e. Arroyo et al., 2013; Baker et al., 2019; Finkelstein et al., 2013; Mayfield et al., 2019; Melis et al., 2009; Woolf et al., 2010), considerable bias in education can come from how a learning interaction is designed.”
- Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review (2023, background): “This harm may come from “allocational” or “representational” aspects (Mayfield et al., 2019) and may eventually derail the purposes of education.”