Work

Exploring demographic influences on fairness perceptions across the ML development lifecycle

Year: 2026

Type: article

Abstract: 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.

Source: AI and Ethics

DOI: 10.1007/s43681-026-01034-1

Authors: Anoop Mishra, Deepak Khazanchi, Abhishek Tripathi

Institutions: University of Nebraska at Omaha, The College of New Jersey

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Peace, justice, and strong institutions

Open Access status: hybrid

Estimated APC paid: $3,190