Blockchain Papers

Asabi Anthony Oluyemi

Abstract This study explores the transformative impact of artificial intelligence (AI) on enhancing demand forecasting and procurement efficiency within health commodity supply chains. It highlights the integration of advanced AI algorithms, including machine learning (ML), natural language processing (NLP) and optimisation techniques, which facilitate more accurate predictions, streamlined sourcing and improved inventory management. The analysis emphasises the essential interplay between technological innovation and ethical practices, underlining the importance of data privacy, transparency, fairness and accountability as foundational elements for trustworthy AI deployment in healthcare procurement. Implementation strategies take into account infrastructure requirements, change management and potential barriers to adoption. The investigation further examines organisational and workforce implications, scalability, sustainability and comparative experiences on both global and local scales, illustrating the complex challenges and opportunities presented by AI in health commodity procurement. Future directions suggest the convergence of AI with Internet of Things, blockchain and cloud computing, advocating for responsible innovation that adheres to ethical standards to optimise supply chain resilience, equity and operational performance in healthcare delivery.

Ahmad J. Alkhodair

Federated learning (FL) enables collaborative model training without centralizing raw training records, but it does not inherently provide verifiable model provenance, enforceable fairness policies, or auditable control over aggregation. This paper presents FairAI, a blockchain- and IPFS-enabled framework that treats each local model as a governed artifact linked to performance and group-fairness metrics, content identifiers, manifests, Groth16 evidence, and smart-contract decisions. Only models approved on-chain and subsequently retrieved and validated through their registered CIDs are eligible for aggregation. The primary real-data evaluation used the Adult and COMPAS datasets under IID and joint label/protected-group non-IID partitions, with ten paired seeds comparing standard FedAvg, post hoc fairness assessment, a pre-aggregation fairness policy gate, and FairFed. Under heterogeneous Adult data, the policy gate reduced the demographic-parity gap from 0.0273 to 0.0127, while accuracy decreased from 0.7740 to 0.7629. Under heterogeneous COMPAS data, the equalized-odds gap decreased from 0.2262 to 0.1226, while accuracy decreased from 0.6495 to 0.5809; the paired accuracy and equalized odds differences remained significant after Holm correction, with adjusted p-values of 0.0318 and 0.0491, respectively. Additional bounded experiments evaluated a small multilayer perceptron, policy threshold sensitivity, logical-client scaling, poisoning, coordinate-wise median aggregation, two native Kubo/IPFS peers, V2 Groth16 verification, and smart-contract overhead. Thirty valid V2 proofs were accepted, six inconsistent cases were rejected, and direct Solidity verification consumed 348,811 gas per measured transaction. A full-path false-metric experiment showed that the proof verifies threshold compliance and artifact binding for supplied values, but does not establish their correct derivation from private data. When an approved artifact became unavailable, FairAI cancelled the round before aggregation and published no global model.