Publication
Title
Explainability methods to detect and measure discrimination in machine learning models
Author
Abstract
Today, it is common to use machine learning models for high-stakes decisions, but this can pose a threat to fairness as these models can amplify bias present in the dataset. At the moment, there is no consensus on a universal method to tackle this, and we argue that this is also not possible as the right method will depend on the context of each case. As a solution, our aim was to bring transparency in the fairness domain, and in earlier work, we proposed a counterfactual-based algorithm (π‘ƒπ‘Ÿπ‘’πΆπ‘œπΉ) to identify bias in machine learning models. This method attempts to counter the disagreement problem in Explainable AI, by reducing the flexibility of the model owner. We envision a future where transparency tools such as the latter are used to perform fairness audits by independent auditors who can judge for each case whether the audit revealed discriminatory patterns or not. This approach would be more in line with the current nature of EU legislation, as its requirements are often too contextual and open to judicial interpretation to be automated.
Language
English
Source (journal)
CEUR workshop proceedings
Source (book)
EWAF’23 : European Workshop on Algorithmic Fairness, June 07–09, 2023, Winterthur, Switzerland
Publication
CEUR , 2023
Volume/pages
3442 , p. 1-5
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identifier
Creation 16.10.2023
Last edited 04.03.2024
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