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Crossing Margins: Intersectional Users' Ethical Concerns about Software

Lauren Olson, Tom P. Humbert, Ricarda Anna-Lena Fischer, Bob Westerveld, Florian Kunneman, Emitzá Guzmán

TL;DR

This work collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority to identify critical concerns.

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 issues 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 starting point to address their ethical concerns. We aggregated and analyzed the intersectional users' ethical concerns over time and developed a prioritization method to identify critical concerns. To achieve this, we collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority. Our findings revealed that intersectional communities report \textit{critical} complaints related to cyberbullying, inappropriate content, and discrimination, highlighting significant flaws in modern software, particularly for intersectional users. Based on these findings, we discuss how to better address the ethical concerns of intersectional users in software development.

Crossing Margins: Intersectional Users' Ethical Concerns about Software

TL;DR

This work collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority to identify critical concerns.

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 issues 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 starting point to address their ethical concerns. We aggregated and analyzed the intersectional users' ethical concerns over time and developed a prioritization method to identify critical concerns. To achieve this, we collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority. Our findings revealed that intersectional communities report \textit{critical} complaints related to cyberbullying, inappropriate content, and discrimination, highlighting significant flaws in modern software, particularly for intersectional users. Based on these findings, we discuss how to better address the ethical concerns of intersectional users in software development.

Paper Structure

This paper contains 39 sections, 9 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Scope and Research Questions
  4. Methodology
  5. Data Collection
  6. Community Detection
  7. App Selection
  8. Data Scraping
  9. Ethical Concern Presence Detection
  10. Sampling
  11. Manual Labeling
  12. Automated Labeling
  13. Ethical Concern Categorization (RQ1)
  14. Filtering
  15. Multi-class Labeling
  16. ...and 24 more sections

Figures (10)

  • Figure 1: Methodology
  • Figure 2: Intersectional community cluster names are to the left of the columns. The columns show which marginalized identities are represented in each cluster.
  • Figure 3: Sample Prompt and Post to the GPT-4 engine to classify ethical concern types. The post is altered to maintain the author's privacy. For the study we used the API rather than the UI that is shown here for visualization purposes. Categories and their definitions were included at the end of the prompt (here excluded for the sake of readability).
  • Figure 4: Ethical Concern Frequency in Full Dataset
  • Figure 5: Ethical Concern Frequency Amongst Apps
  • ...and 5 more figures