An Investigation of Experiences Engaging the Margins in Data-Centric Innovation

2025-01-13

AI Summary

This study addresses fairness risks arising from the underrepresentation of marginalized groups in data-driven innovation. Through a mixed-methods empirical investigation involving 261 data practitioners—including anonymous surveys, descriptive statistics, and intersectional analysis—it systematically identifies how age and social identity shape data representativeness practices: younger professionals and those holding minoritized identities report higher frequencies of representational gaps and demonstrate greater initiative in pursuing alternative solutions. Moving beyond technocentric paradigms, the research integrates sociological dimensions—particularly the intersectionality of age and identity—into the core of data governance frameworks. It thus provides both theoretical grounding and empirical evidence for advancing inclusive, equity-oriented data practices.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Security and Privacy: Data transparency and provenance
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Data-centric technologies provide exciting opportunities, but recent research has shown how lack of representation in datasets, often as a result of systemic inequities and socioeconomic disparities, can produce inequitable outcomes that can exclude or harm certain demographics. In this paper, we discuss preliminary insights from an ongoing effort aimed at better understanding barriers to equitable data-centric innovation. We report findings from a survey of 261 technologists and researchers who use data in their work regarding their experiences seeking adequate, representative datasets. Our findings suggest that age and identity play a significant role in the seeking and selection of representative datasets, warranting further investigation into these aspects of data-centric research and development.

Problem

• Data-Driven Innovation
• Fair Participation
• Social Inequality

Innovation

• Data-driven Innovation
• Equitable Engagement
• Age and Identity Factors