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.

The main goal of this project is to create a new software artefact: a custom Generative Pre-trained Transformer (GPT) for developers to discuss and solve ethical issues through AI engineering. This conversational agent will provide developers with practical application on (1) how to comply with legal frameworks which regard AI systems (like the EU AI Act [8] and GDPR [11]) and (2) present alternate ethical perspectives to allow developers to understand and incorporate alternate moral positions. In this paper, we provide motivation for the need of such an agent, detail our idea and demonstrate a use case. The use of such a tool can allow practitioners to engineer AI solutions which meet legal requirements and satisfy diverse ethical perspectives.

Abstract This work analyzes ethical concerns found in users’ app store reviews. We performed this study because ethical concerns in mobile applications (apps) are widespread, pose severe threats to end users and society, and lack systematic analysis and methods for detection and classification. In addition, app store reviews allow practitioners to collect users’ perspectives, crucial for identifying software flaws, from a geographically distributed and large-scale audience. For our analysis, we collected five million user reviews, developed a set of ethical concerns representative of user preferences, and manually labeled a sample of these reviews. We found that (1) users highly report ethical concerns about censorship, identity theft, and safety (2) user reviews with ethical concerns are longer, more popular, and lowly rated, and (3) there is high automation potential for the classification and filtering of these reviews. Our results highlight the relevance of using app store reviews for the systematic consideration of ethical concerns during software evolution.

Studies show Visual Question Answering (VQA) systems are valuable tools for people with visual impairments to quickly obtain information from an image. In this poster, we present our ongoing work towards developing uses of live photos to mitigate quality issues in photography from people with visual impairments for VQA. New contributions building on our prior research include an expanded live photos dataset, a more in-depth analysis of VQA results, and an analysis of features of our live photos compared with existing data collected from people with visual impairments. We show that live photos are a promising method for improving accuracy of VQA and that our sample live photos mimic the types of images taken in real-world settings by people with visual impairments for the task of VQA.