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Traditional machine learning algorithms use data from databases that aremutable, and therefore the data cannot be fully trusted. Also, the machinelearning process is difficult to automate. This paper proposes building atrustable machine learning system by using blockchain technology, which canstore data in a permanent and immutable way. In addition, smart contracts areused to automate the machine learning process. This paper makes threecontributions. First, it establishes a link between machine learning technologyand blockchain technology. Previously, machine learning and blockchain havebeen considered two independent technologies without an obvious link. Second,it proposes a unified analytical framework for trustable machine learning byusing blockchain technology. This unified framework solves both thetrustability and automation issues in machine learning. Third, it enables acomputer to translate core machine learning implementation from a single threadon a single machine to multiple threads on multiple machines running withblockchain by using a unified approach. The paper uses association rule miningas an example to demonstrate how trustable machine learning can be implementedwith blockchain, and it shows how this approach can be used to analyze opioidprescriptions to help combat the opioid crisis.

A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain