Individual fairness testing (Ift) is a framework to find discriminatory instances within a given classifier. In this paper, we show our idea of a Ift framework, that integrates the notion of data validity, termed "Individual Fairness Testing with Data Validity (Ift-v)". We develop a solid foundation of Ift-v and demonstrate the feasibility of Ift-v. Our preliminary evaluation with Ift-v reveals the possibility that many of discriminatory instances detected by state-of-the-art Ift algorithms are considered invalid. These findings prompt a re-think of the current Ift framework, suggesting a transition from solely focusing on the discovery of discriminatory instances to the consideration of valid ones.

As machine learning (ML) systems are increasingly used in various fields, including tasks with high social impact, concerns about their fairness are growing. To address these concerns, individual fairness testing (IFT) has been introduced to identify individual discriminatory instances (IDIs) that indicate the violation of individual fairness in a given ML classifier. In this paper, we propose a black-box testing algorithm for IFT, named Aft (short for Approximation-guided Fairness Testing). Aft constructs approximate models based on decision trees, and generates test cases by sampling paths of the decision trees. Our evaluation by experiments confirms that Aft outperforms the state-of-the-art black-box IFT algorithm ExpGA both in efficiency (by 3.42 times) and diversity of IDIs identified by algorithms (by 1.16 times).

This paper presents a unified framework for capturing a variety of graph reconfiguration problems in terms of firstorder expressible properties and proposes a Boolean encoding for formulas in the first-order logic of graphs based on the exploitation of fundamental properties of graphs. We show that a variety of graph reconfiguration problems captured in our framework can be computed in a unified way by combining our encoding and Boolean satisfiability solver in a bounded model checking approach but allowing us to use quantifiers and predicates on vertices to express reconfiguration properties.