Meta-analysis reveals the impacts of AI transparency interventions on digital labor, highlighting shifts in perceived fairness, autonomy, and performance.
This repository contains the data and analysis code for a random-effects meta-analysis of the effect of transparency/explainability interventions on worker outcomes (perceived fairness, trust, autonomy, performance) in algorithmic digital labor, submitted to PeerJ Computer Science. Effect sizes (Hedges' g) and moderator codes were extracted from k=40 empirical studies (2016-2024) following PRISMA 2020. The package includes raw coded data (CSV), processed data, analysis code (Python 3.8, numpy/scipy), manuscript tables and figures, and full reproduction instructions (see README.md).
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haoxiang Ma (2026) studied this question.
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