AKWI-Tagungsband zur 35. AKWI-Jahrestagung, 2022, S. 316–320
AKWI 2022
Data Synthesis for Fairness Audits of Learning Analytics Algorithms

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						@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 03.02.23)

Abstract

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

Schlüsselwörter

Learning analytics, synthetic data, fairness audit

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