Tom P. Humbert

L. Olson, R. A.-L. Fischer, T. P. Humbert, F. Kunneman and E. Guzmán, "Whose voices are heard? Gender disparities in platform-facilitated discrimination and content moderation," Information and Software Technology, 2025, art. no. 107899.

Abstract: Historically, cisgender men have maintained systemic social, cultural, and political privilege over other genders. Online discrimination serves as a mechanism for reinforcing this dominance. Content moderation plays a crucial role in shaping online experiences, yet the ways it may perpetuate or mitigate discrimination remain underexplored. This study examines how content moderation and discussions of discrimination vary across gendered online communities, with a focus on identifying differential impacts by gender group. We analyzed 124 subreddits spanning three gender groups—women, men, and gender minorities (GM). The analysis included manual annotation of 1,535 posts and machine learning classification of an additional 6,613 posts to assess the prevalence of user discussions regarding online discrimination and content moderation. Women were most likely to report top-down moderation issues, such as bans and content removal, while GM users engaged more frequently with general moderation concerns. Time series analysis revealed that complaints about content moderation have increased over time, with the steepest rise among women users. These patterns demonstrate that moderation policies and enforcement impact gender groups differently. Our findings highlight the need for improvements in software engineering and user experience design for content moderation tools. Enhancing transparency, promoting equity, and enabling more user-driven moderation experiences are critical steps toward protecting marginalized groups against discrimination online.

Y. Elias, T. P. Humbert, L. Olson and E. Guzmán, "What is Unethical About Software? User Perceptions in the Netherlands," 2025 IEEE/ACM 47th ICSE: Software Engineering in Society (ICSE-SEIS), Ottawa, ON, Canada, 2025, pp. 118-129.

Abstract: Software has the potential to improve lives. Yet, unethical and uninformed software practices are at the root of an increasing number of ethical concerns. Despite its pervasiveness, few research has analyzed end-users perspectives on the ethical issues of the software they use. We address this gap, and investigate end-user's ethical concerns in software through 19 semi-structured interviews with residents of the Netherlands. We ask a diverse group of users about their ethical concerns when using everyday software applications. We investigate the underlying reasons for their concerns and what solutions they propose to eliminate them. We find that our participants actively worry about privacy, transparency, manipulation, safety and inappropriate content; with privacy and manipulation often being at the center of their worries. Our participants demand software solutions to improve information clarity in applications and provide more control over the user experience. They further expect larger systematic changes within software practices and government regulation.

Ö. Karaçam, T. P. Humbert and E. Guzmán, "Uncovering Patterns in Users' Ethical Concerns About Software," 2024 IEEE 32nd International Requirements Engineering Conference (RE), Reykjavik, Iceland, 2024, pp. 466-474.

Abstract: 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.