Bias and Discrimination in Decentralized AI Decision Systems
DOI:
https://doi.org/10.63345/Keywords:
Decentralized AI, Discrimination, Fairness, Federated Learning, DAO Governance, Blockchain, Non-IID DataAbstract
Decentralized AI decision systems—spanning federated learning, peer-to-peer optimization, DAO-governed models, and blockchain-orchestrated inference—promise resilience, privacy, and transparency by distributing data, compute, and control. Yet decentralization does not automatically guarantee fairness. This paper examines how bias and discrimination emerge and persist when decision-making is pushed to the edge or collectively governed, and how those dynamics differ from centralized pipelines. We synthesize the literature on algorithmic bias, fairness metrics, federated learning with non-IID data, and token-based governance to articulate a socio-technical framework for risk.
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