Algorithmic Bias in Education

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 expande...

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Vydáno v:International journal of artificial intelligence in education Ročník 32; číslo 4; s. 1052 - 1092
Hlavní autoři: Baker, Ryan S., Hawn, Aaron
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer New York 01.12.2022
Springer
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Automation
Bias
Computer Science
Computers and Education
Decision making
Disabilities
Education
Educational Practices
Educational Technology
Educational Testing
Gender
Learning Processes
Machine learning
Mathematics
Military Personnel
Race
Racism
Sex
Socioeconomic Status
Standardized Tests
Statistical Bias
Students
User Interfaces and Human Computer Interaction
Veterans
Algorithmic bias
Algorithmic fairness
Machine learning
Artificial intelligence and education
ISSN:1560-4292, 1560-4306
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Shrnutí: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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ISSN:1560-4292
1560-4306
DOI:10.1007/s40593-021-00285-9