Promoting Distributed Trust in Machine Learning and Computational Simulation

Abstract

Policy decisions are increasingly dependent on the outcomes of simulations and/or machine learning models. The ability to share and interact with these outcomes is relevant across multiple fields and is especially critical in the disease modeling community where models are often only accessible and workable to the researchers that generate them. This work presents a blockchain-enabled system that establishes a decentralized trust between parties involved in a modeling process. Utilizing the OpenMalaria framework, we demonstrate the ability to store, share and maintain auditable logs and records of each step in the simulation process, showing how to validate results generated by computational workers. We also show how the system monitors worker outputs to rank and identify faulty workers via comparison to nearest neighbors or historical reward spaces as a means of ensuring model quality.