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Cited by 994Ethics and Social Impacts of AIArtificial Intelligence in EducationOnline Learning and Analytics

The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.

Algorithmic Bias in Education

Open access
Baker, Ryan S., Hawn, Aaron·2022·International Journal of Artificial Intelligence in Education
DOI 10.1007/s40593-021-00285-9
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Abstract

In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review focuses instead on solidifying the current understanding of the concrete impacts of algorithmic bias in education—which groups are known to be impacted and which stages and agents in the development and deployment of educational algorithms are implicated. We discuss theoretical and formal perspectives on algorithmic bias, connect those perspectives to the machine learning pipeline, and review metrics for assessing bias. Next, we review the evidence around algorithmic bias in education, beginning with the most heavily-studied categories of race/ethnicity, gender, and nationality, and moving to the available evidence of bias for less-studied categories, such as socioeconomic status, disability, and military-connected status. Acknowledging the gaps in what has been studied, we propose a framework for moving from unknown bias to known bias and from fairness to equity. We discuss obstacles to addressing these challenges and propose four areas of effort for mitigating and resolving the problems of algorithmic bias in AIED systems and other educational technology.

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Citations

Cites

Show all 122 references

Research works printed but not linked: 7

  1. Bird, S., Dale, R., Dorr, B. J., Gibson, B., Joseph, M. T., Kan, M. Y., Lee, D., Powley, B., Radev, D. R., & Tan, Y. F. (2008). No Title. Proceedings of the 6th International Conference on Language Resources and Evaluation, 1755–1759.
  2. Cramer, H., Holstein, K., Vaughan, J. W., Daumé, H., Dudik, M., Wallach, H., Reddy, S., & Jean, G.-G. [The Conference on Fairness, Accountability, and Transparency (FAT*)]. (2019). FAT* 2019 translation tutorial: Challenges of incorporating algorithmic fairness [video]. YouTube. https://youtu.be/UicKZv93SOY
  3. Crawford, K. [The Artificial Intelligence Channel]. (2017). The Trouble with Bias - NIPS 2017 Keynote - Kate Crawford [Video]. YouTube. https://youtu.be/fMym_BKWQzk
  4. Holstein, K. & Doroudi, S. (in press). Equity and artificial intelligence in education: Will “AIEd” Amplify or Alleviate Inequities in Education? Invited chapter in Porayska-Pomsta, K. & Holmes, W. (Eds.), Ethics in AIED: Who Cares? Data, algorithms, equity and biases in educational contexts. Routledge Press.
  5. Howley, I. (2018) If an algorithm is openly accessible, and no one can understand it, is it actually open? In Artificial Intelligence in Education Workshop on Ethics in AIED 2018.
  6. Li, X., Song, D., Han, M., Zhang, Y., & Kizilcec, R. F. (2021). On the limits of algorithmic prediction across the globe. arXiv preprint arXiv:2103.15212. · arXiv
  7. Soundarajan, S., & Clausen, D. L. (2018). Equal Protection Under the Algorithm: A Legal-Inspired Framework for Identifying Discrimination in Machine Learning. Proceedings of the 35th International Conference on Machine Learning.

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Metadata

Authors
Baker, R. S., & Hawn, A.
Affiliations
University of Pennsylvania (US)
Corresponding author
Baker, Ryan S.
ORCID
Baker, Ryan S.
Venue
International Journal of Artificial Intelligence in Education
Publisher
Elsevier BV
Published
2022-12
Volume
32
Issue
4
Pages
1052-1092
ISSN
1560-4292 (ISSN-L)
DOI
10.1007/s40593-021-00285-9
OpenAlex
W3214911045
Semantic Scholar
38472f9d5ba1c93fcbc33b294ff9747a63988d3a
Cited by
993
References
122
Citations by year
2026: 393 · 2025: 276 · 2024: 165 · 2023: 96 · 2022: 59
FWCI
101.28
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
  • EdArXiv (OSF Preprints) (submittedVersion)
Funders
  • Schmidt Futures
  • University of Pennsylvania
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
algorithmic bias, artificial intelligence in education, algorithmic fairness, bias metrics, racial and ethnic bias, gender bias, nationality bias, socioeconomic status, disability, military-connected students, educational equity, bias mitigation, machine learning pipelines

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