Cited by 30Ethics and Social Impacts of AIDigital Education and SocietyGlobal Educational Policies and Reforms

The politics and reciprocal (re) configuration of accountability and fairness in data-driven education

Open access
Sahlgren, Otto·2023·Learning, Media and Technology

Abstract

As awareness of bias in educational machine learning applications increases, accountability for technologies and their impact on educational equality is becoming an increasingly important constituent of ethical conduct and accountability in education. This article critically examines the relationship between so-called algorithmic fairness and algorithmic accountability in education. I argue that operative political meanings of accountability and fairness are constructed, operationalized, and reciprocally configured in the performance of algorithmic accountability in education. Tools for measuring forms of unwanted bias in machine learning systems, and technical fixes for mitigating them, are value-laden and may conceal the politics behind quantifying educational inequality. Crucially, some approaches may also disincentivize systemic reforms for substantive equality in education in the name of accountability.

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Crossref · 10.1080/17439884.2021.1986065

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Authors
Sahlgren, O.
Affiliations
Tampere University (FI)
Corresponding author
Sahlgren, Otto
Venue
Learning, Media and Technology
Published
2021-10-01
Volume
48
Issue
1
Pages
95-108
ISSN
  • 1743-9884 (ISSN-L)
  • 1743-9892
OpenAlex
W3204629139
Cited by
30
References
50
Citations by year
2026: 5 · 2025: 7 · 2024: 6 · 2023: 6 · 2022: 6
FWCI
5.63
Type
journal article
Language
en
Open access
Locations
  • Learning Media and Technology (publishedVersion, cc-by)
  • Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
Funders
Academy of Finland (326584)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Digital Education and Society
  • Social Sciences › Political Science and International Relations › Global Educational Policies and Reforms
Keywords
algorithmic accountability, algorithmic fairness, machine learning bias, educational inequality, substantive equality, bias mitigation, systemic reform

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