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227  \n© 2024 Vilnius University  \nDOI: [https://doi.org/10.15388/24-INFOR553](https://doi.org/10.15388/24-INFOR553)  \nAdvancing Research Reproducibility in Machine Learning through Blockchain Technology  \nErnestas FILATOVAS∗ , Linas STRIPINIS, Francisco ORTS, Remigijus PAULAVIČIUS  \n1 Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania  \ne-mail: ernestas.fi[latovas@mif.vu.lt](latovas@mif.vu.lt), [linas.stripinis@mif.vu.lt](linas.stripinis@mif.vu.lt), [francisco.orts@mif.vu.lt](francisco.orts@mif.vu.lt),  \n[remigijus.paulavicius@mif.vu.lt](remigijus.paulavicius@mif.vu.lt)  \nReceived: February 2024; accepted: April 2024  \nAbstract. Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peerto-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence.  \nKey words: machine learning, reproducibility, reproducible research, blockchain, distributed ledger technology, interoperability, blockchain-based platform, hyperledger fabric.  \n1. Introduction  \nIn recent years, the field of machine learning (ML) has seen remarkable advances, with algorithms achieving unprecedented levels of performance in various tasks such as image analysis (Gudžius et al., 2021; Gudzius et al., 2022), voice (Mehrish et al., 2023), and face recognition (Liu et al., 2023), planning (Usuga Cadavid et al., 2020), scheduling (Bertolini et al., 2021), etc. The advent of ML, particularly deep learning (DL), has  \n∗Corresponding author.  \n228 E. Filatovas et al.  \nemerged as the cornerstone of artificial intelligence (AI) due to advances in computational capabilities (Zhang and Lu, 2021) . However, the widespread dependence on vast datasets and intricate models presents a formidable challenge to reproducibility in ML research. Regrettably, as ML and other AI domains continue to gain prominence, researchers face a reproducibility crisis (RC) (Hutson, 2018) . This crisis is characterized by the daunting challenge of replicating critical findings, worsened by the gap between published results and the underlying models, data, parameter configurations and other essential experimental components, frequently missing crucial details.  \nTo address issues related to RC, both the research community and industry have actively explored a wide variety of solutions. Traditionally, researchers have often resorted to depositing their code and data into repositories such as GitLab or GitHub. Howe","cbCaidtbsDtF6H6J","https://ap.wps.com/l/cbCaidtbsDtF6H6J","pdf",1539806,27,"English","# Introduction\n## Reproducibility crisis in machine learning\n## Limitations of code/data repositories and existing platforms\n## Need for transparency, tracking, and auditability","[{\"question\":\"What causes the reproducibility crisis in machine learning research?\",\"answer\":\"Researchers struggle to reproduce key findings because publications often lack critical details, including the connection to underlying models, datasets, parameter settings, and essential experimental components.\"},{\"question\":\"Why are existing code and data repositories sometimes insufficient?\",\"answer\":\"They frequently do not capture crucial information such as runtime environments, contextual details, and system information, and journal-linked datasets may become unreliable due to broken links.\"},{\"question\":\"How does the proposed blockchain-based platform address reproducibility?\",\"answer\":\"It uses a peer-to-peer blockchain network to improve accessibility of models and data, facilitate experiment tracking, and allow auditing of experimental results, validated via Hyperledger Fabric simulations.\"}]","Advancing Research Reproducibility in Machine Learning through Blockchain Technology | PDF",68]