The many dimensions of algorithmic fairness in educational applications
By Anastassia Loukina, Nitin Madnani, Klaus Zechner. 2019
61 citations recorded by the source corpus.
The issues of algorithmic fairness and bias have recently featured prominently in many publications highlighting the fact that training the algorithms for maximum performance may often result in predictions that are biased against various groups. Educational applications based on NLP and speech processing technologies often combine multiple complex machine learning algorithms and are thus vulnerable to the same sources of bias as other machine learning systems. Yet such systems can have high impact on people’s lives especially when deployed as part of high-stakes tests. In this paper we discuss different definitions of fairness and possible ways to apply them to educational applications. We then use simulated and real data to consider how test-takers’ native language backgrounds can affect their automated scores on an English language proficiency assessment. We illustrate that total fairness may not be achievable and that different definitions of fairness may require different solutions.
Subfields: Artificial Intelligence, Safety Research, 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 Artificial Intelligence
- Ruslan Salakhutdinov
- Yejin Choi
- Hal Daumé Iii
- Percy Liang
- Noah A. Smith
- Mohit Bansal
- Yoav Goldberg
- Yoshua Bengio
In Safety Research
- Hanna M. Wallach
- Su Lin Blodgett
- Alan W. Black
- Ryan Shaun Joazeiro de Baker
- Ruslan Salakhutdinov
- Hal Daumé Iii
- Aaron Hawn
- Solon Barocas
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): “…(2019); Bhargava and Forsyth (2019); Cao and Daumé (2019); Font and Costa-jussà (2019); Garg et al. (2019); Garimella et al. (2019); Liu et al. (2019); Loukina et al. (2019); Mehrabi et al. (2019); Nozza et al. (2019); Sap et al. (2019); Sheng et al. (2019); Stanovsky et al. (2019); Vaidya et al.…”
- An automated essay scoring systems: a systematic literature review (2021, background): “Loukina et al. (2015) worked on feature selection from speech data and trained”
- Case Study: Deontological Ethics in NLP (2020, background): “, 2017; Mieskes, 2017) and specific domains like education (Mayfield et al., 2019; Loukina et al., 2019), healthcare (Šuster et al.”
- Algorithmic Bias in Education (2021, background): “Specifically, technical obstacles to the use of fairness metrics manifest in several “impossibility” results (Chouldechova, 2017; Kleinberg et al., 2017; Berk et al., 2018; Loukina et al., 2019; Lee & Kizilcec, 2020, Darlington, 1971), where satisfaction of one statistical criterion of fairness make”