As machine learning software increasingly shapes crucial decisions in our daily lives, ensuring the fairness of these decisions is paramount. Individual fairness guarantees non-discrimination based on protected attributes, such as race or gender. Discriminatory instances reveal individual discrimination included in machine learning software. Existing methods for detecting individual discrimination often rely on initial "seed"instances, which are data points selected from the dataset that have more likelihood of exhibiting discrimination. These seed instances are then used as the basis for generating more discriminatory instances. While effective, this approach may inadvertently overlook discrimination embedded within seemingly fair non-seed instances. To overcome this limitation, this paper proposes FairBS, an approach that utilizes non-seed instances to generate discriminatory instances using counterfactual analysis. FairBS first constructs an explainer based on dataset input features and a model under test. It then generates an input instance and checks it for discrimination. If the input instance is non-discriminatory, FairBS uses the explainer to create counterfactual examples of that instance, by causing minimal perturbation to its feature values, which then produce other instances with opposite predictions. Extensive experiments on five datasets and five machine learning models demonstrate that our proposed approach outperforms state-of-the-art methods in both efficiency and effectiveness across all datasets and models. Our approach generates an average of � 13.44 more discriminatory instances at � 14.51 faster speed compared to existing seed-based methods. These findings indicate that FairBS expands the boundaries of fairness testing beyond the discriminatory seed instances, providing a powerful tool that can be used by software engineers to better ensure fairness in machine learning software. © 2013 IEEE.