Measuring Perceived Fairness Of The Machine Learning Development Process: Conceptual Framework and Empirical Validation

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Abstract
Fairness in Machine Learning (ML) development is a critical but subjective concern, shaped by biases in data, practices, and assumptions. Although algorithmic fairness has gained attention, empirical insights into how ML practitioners and domain experts perceive fairness across development remain limited. Using a sociotechnical lens, this study examines how fairness perceptions evolve during ML development, shifting attention from outcomes to processes to support human-centered evaluation. This research introduces and validates the TAR framework, which operationalizes fairness perceptions across three dimensions: transparency, accountability, and representativeness. Grounded in theory, three validated survey scales measure these perceptions systematically. The findings offer a nuanced understanding of fairness, enabling more inclusive ML design. By incorporating practitioner and subject matter expert perspectives, the TAR framework provides a human-centered tool for evaluating fairness, guiding ethical design decisions, and fostering equitable ML practices.

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