Abstract Many modern software applications present numerous ethical concerns due to conflicts between users’ values and companies’ priorities. Intersectional communities, those with multiple marginalized identities, are disproportionately affected by these ethical issues, leading to legal, financial, and reputational consequences for software companies, as well as real-world harm for intersectional users. Historically, the voices of intersectional communities have been systematically marginalized and excluded from contributing their unique perspectives to software design, perpetuating software-related ethical concerns. This work aims to fill the gap in research on intersectional users’ software-related perspectives and provide software practitioners with a methodology for analyzing intersectional voices in software ethics discourse. We collected 36,777 posts from over 700 intersectional subreddits discussing software applications and utilized large language models to identify ethical concerns in these posts. We then applied regression models with counterfactual analysis to examine how intersectional identity dimensions shape the amplification or suppression of ethical concern expression across software genres, and conducted a time-series analysis to examine how concern expression varies over time in relation to real-world events. As a case study in the social media domain, we further demonstrate how identified ethical concerns can be prioritized to surface issues warranting timely developer attention, validated against survey-derived ground truth. Together, these analyses form the basis of a nascent feedback-driven framework for assessing whether software systems are meeting the needs of intersectional users.

Abstract Software is a crucial component of modern everyday life. However, ethical issues in software, such as privacy issues, censorship and behavior manipulation are prevalent in software applications. To develop ethical software, it is essential to understand the ethical concerns of its end-users. To provide these insights, we conducted a survey among 725 participants and analyze which ethical concerns they have regarding software and why, as well as the actions they are willing to take when confronted with ethical issues in software. The results indicate that privacy, scam and misinformation are important ethical concerns for the wide majority, with scam prompting the strongest reaction in all unethical software scenarios. Reasons for these concerns included—but are not limited to—the exposure of personal sensitive information, perceived societal harm and fear of material loss. The most common reactions to ethical issues in software are to stop using the software and encourage friends and family to do the same. We also find that perceived importance and reactions to unethical scenarios in software products varies significantly by gender, education, and continent.

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.

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.