Bibtex
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English
@Inbook{Fernsel+Simbeck,
Cite-key = "fernsel2022",
Year= "2022",
Volume= "AKWI-Tagungsband zur 35. AKWI-Jahrestagung",
Pages= "S. 316–320",
Journal = "Monographien",
Title= "Data Synthesis for Fairness Audits of Learning Analytics Algorithms",
Author= "Linda Fernsel, Katharina Simbeck ",
Doi= "https://doi.org/10.30844/AKWI_2022_21",
Abstract= "The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be available (in large quantities) due to privacy concerns. Our poster shares our findings on how relational data for fairness audits of Learning Analytics systems can be synthesized from little pre-existing data, using the most promising available data synthesizers.",
Keywords= "Learning analytics, synthetic data, fairness audit",
}
Linda Fernsel, Katharina Simbeck(2022): Data Synthesis for Fairness Audits of Learning Analytics Algorithms. AKWI-Tagungsband zur 35. AKWI-Jahrestagung(2022), S. S. 316–320. Online: https://doi.org/10.30844/AKWI_2022_21 (Abgerufen 15.11.24)
Open Access
The purpose of methods of fairness auditing is to uncover to what extent Learning Analytics algorithms are fair. Fairness auditing methods often rely on pre-existing test data. In the context of Learning Analytics auditing, learning data is needed for testing. However, learning data might not be available (in large quantities) due to privacy concerns. Our poster shares our findings on how relational data for fairness audits of Learning Analytics systems can be synthesized from little pre-existing data, using the most promising available data synthesizers.
Learning analytics, synthetic data, fairness audit
