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<p>Perceived fairness in machine learning (ML) systems represents a sociotechnical phenomenon, shaped by both organizational dynamics and technical practices. Building on prior research that formalizes fairness as experienced within ML development, this study investigates perceptual gaps through a large-scale survey of around 123&#xa0;ML practitioners. Shifting from outcome-focused fairness research, this work centers on how stakeholders interpret and experience fairness throughout the ML development lifecycle. Specifically, it examines how practitioner demographics influence fairness perceptions within real-world ML projects. Drawing from sociotechnical literature, this study develops and validates a survey instrument to capture these perceptions throughout the ML development lifecycle. Analysis reveals that demographic factors, including experience, gender, and education, significantly affect how fairness is perceived. The findings underscore the importance of inclusive design practices and point to actionable pathways for policy reform, targeted training, and organizational strategies aimed at promoting equity, transparency, and accountability in ML systems.</p>

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Exploring demographic influences on fairness perceptions across the ML development lifecycle

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月16日

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  • Anoop Mishra,
  • Deepak Khazanchi,
  • Abhishek Tripathi

摘要

Perceived fairness in machine learning (ML) systems represents a sociotechnical phenomenon, shaped by both organizational dynamics and technical practices. Building on prior research that formalizes fairness as experienced within ML development, this study investigates perceptual gaps through a large-scale survey of around 123 ML practitioners. Shifting from outcome-focused fairness research, this work centers on how stakeholders interpret and experience fairness throughout the ML development lifecycle. Specifically, it examines how practitioner demographics influence fairness perceptions within real-world ML projects. Drawing from sociotechnical literature, this study develops and validates a survey instrument to capture these perceptions throughout the ML development lifecycle. Analysis reveals that demographic factors, including experience, gender, and education, significantly affect how fairness is perceived. The findings underscore the importance of inclusive design practices and point to actionable pathways for policy reform, targeted training, and organizational strategies aimed at promoting equity, transparency, and accountability in ML systems.