Ethical concerns about software applications, e.g., worries about privacy breaches, user manipulation, and discrimination, have gained prominence recently. Research shows that users voice these concerns in app reviews and that they can be detected using machine learning and deep learning techniques. These techniques usually operate as black-boxes, making it difficult to understand the context of users’ ethical concerns. We address this issue by presenting a transparent approach that uses pattern mining and graph theory to yield additional context to the ethical concern classifications made by machine learning algorithms. We compare a simple frequent pattern mining and a high-utility mining algorithm and assess the resulting rules through commonly used metrics. Finally, we visualize and interpret preliminary results in an interactive graph. We mined 3,101 reviews of ten popular apps mentioning diverse ethical concerns and present the results for two apps in detail. Our results show that pattern mining algorithms and graph visualizations are promising directions for detecting contextual information of ethical concerns about software. This work is a step toward ensuring that ethical concerns are methodically thought through and integrated into the software development life cycle.