Concolic Testing on Individual Fairness of Neural Network Models.

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Bibliographic Details
Title:	Concolic Testing on Individual Fairness of Neural Network Models.
Authors:	HUANG, MING-I.¹ 111356047@nccu.edu.tw, HONG, CHIH-DUO¹ chihduo@nccu.edu.tw, YU, FANG¹ yuf@nccu.edu.tw
Source:	Journal of Information Science & Engineering. May2026, Vol. 42 Issue 3, p665-680. 16p.
Subjects:	Fairness, Formal verification, Deep learning, Discrimination (Sociology), Computer software testing, Algorithmic bias, Artificial neural networks
Abstract:	This paper introduces PyFair, a formal framework for evaluating and verifying individual fairness of Deep Neural Networks (DNNs). By adapting the concolic testing tool PyCT, we generate fairness-specific path constraints to systematically explore DNN behaviors. Our key innovation is a dual network architecture that enables comprehensive fairness assessments and provides completeness guarantees for certain network types. We evaluate PyFair on 25 benchmark models, including those enhanced by existing bias mitigation techniques. Results demonstrate PyFair's efficacy in detecting discriminatory instances and verifying fairness, while also revealing scalability challenges for complex models. This work advances algorithmic fairness in critical domains by offering a rigorous, systematic method for fairness testing and verification of pre-trained DNNs. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper introduces PyFair, a formal framework for evaluating and verifying individual fairness of Deep Neural Networks (DNNs). By adapting the concolic testing tool PyCT, we generate fairness-specific path constraints to systematically explore DNN behaviors. Our key innovation is a dual network architecture that enables comprehensive fairness assessments and provides completeness guarantees for certain network types. We evaluate PyFair on 25 benchmark models, including those enhanced by existing bias mitigation techniques. Results demonstrate PyFair's efficacy in detecting discriminatory instances and verifying fairness, while also revealing scalability challenges for complex models. This work advances algorithmic fairness in critical domains by offering a rigorous, systematic method for fairness testing and verification of pre-trained DNNs. [ABSTRACT FROM AUTHOR]
ISSN:	10162364
DOI:	10.6688/JISE.202605_42(3).0011