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

TL;DR

This paper introduces a unified framework combining blockchain and machine learning to enhance trustworthiness and automation, demonstrated through association rule mining for opioid prescription analysis.

Contribution

It establishes a novel link between machine learning and blockchain, creating a unified framework for trustable and automated machine learning systems.

Findings

Blockchain ensures data immutability for trustable ML

Smart contracts automate ML processes

Framework enables multi-machine ML implementation

Abstract

Traditional machine learning algorithms use data from databases that are mutable, and therefore the data cannot be fully trusted. Also, the machine learning process is difficult to automate. This paper proposes building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts are used to automate the machine learning process. This paper makes three contributions. First, it establishes a link between machine learning technology and blockchain technology. Previously, machine learning and blockchain have been considered two independent technologies without an obvious link. Second, it proposes a unified analytical framework for trustable machine learning by using blockchain technology. This unified framework solves both the trustability and automation issues in machine learning. Third, it enables a computer to translate core machine learning implementation from a single thread on a single machine to multiple threads on multiple machines running with blockchain by using a unified approach. The paper uses association rule mining as an example to demonstrate how trustable machine learning can be implemented with blockchain, and it shows how this approach can be used to analyze opioid prescriptions to help combat the opioid crisis.