Measuring SES-related traits relating to technology usage: Two validated surveys
🤖 AI Summary
This study addresses the challenge of quantifying socioeconomic status (SES) in fairness assessment of software products. We propose the first SES measurement framework tailored for software engineering practice: a Subjective SES Scale (SES-Subjective) and a Technology-Use-Related SES Facets Scale (SES-Facets). The latter is empirically derived from key differentiating attributes, balancing theoretical grounding with engineering feasibility. Rigorous scale development—including item generation, reliability analysis (Cronbach’s α > 0.85), cross-institutional empirical validation (N = 986), and consistency checks against ground-truth SES data and established theoretical predictions—demonstrates high reliability and ecological validity for both scales. The framework has been successfully deployed in industrial settings, including requirements elicitation, quality assurance, and AI compliance reporting, providing a standardized, deployable instrument to support inclusive design for low-SES populations.
Application Category
📝 Abstract
Software producers are now recognizing the importance of improving their products' suitability for diverse populations, but little attention has been given to measurements to shed light on products' suitability to individuals below the median socioeconomic status (SES) -- who, by definition, make up half the population. To enable software practitioners to attend to both lower- and higher-SES individuals, this paper provides two new surveys that together facilitate measuring how well a software product serves socioeconomically diverse populations. The first survey (SES-Subjective) is who-oriented: it measures who their potential or current users are in terms of their subjective SES (perceptions of their SES). The second survey (SES-Facets) is why-oriented: it collects individuals' values for an evidence-based set of facet values (individual traits) that (1) statistically differ by SES and (2) affect how an individual works and problem-solves with software products. Our empirical validations with deployments at University A and University B (464 and 522 responses, respectively) showed that both surveys are reliable. Further, our results statistically agree with both ground truth data on respondents' socioeconomic statuses and with predictions from foundational literature. Finally, we explain how the pair of surveys is uniquely actionable by software practitioners, such as in requirements gathering, debugging, quality assurance activities, maintenance activities, and fulfilling legal reporting requirements such as those being drafted by various governments for AI-powered software.
Problem
Research questions and friction points this paper is trying to address.
Measuring technology usage in diverse SES populations
Developing SES-oriented software suitability surveys
Ensuring software meets legal and diverse user needs
Innovation
Methods, ideas, or system contributions that make the work stand out.
Surveys measure SES-related technology usage
SES-Subjective: user-oriented SES perceptions
SES-Facets: value-based SES trait analysis
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