DInEMMo: Decentralized Incentivization for Enterprise Marketplace Models

Abstract

Today, Machine learning (ML) / Artificial Intelligence (AI) has revolutionized the way through which data is perceived. Enterprises are using ML models to gain insights from the data and build applications of highest quality and accuracy. In this process, they are trying to seek more data to derive robust conclusions. However, relevant data are privately held and resides with an organization's premise which thwarts the development of accurate models. Decentralized AI has become an attractive technological trend for enterprises as it ensures model improvement and creates a demand for them through a marketplace. Nonetheless, its potential can be unleashed if there is a massive user participation enabled through fair rewards to its contributors. Motivated by these observations, in this paper, we present DInEMMo, a solution that is built on the convergence of decentralized AI and Blockchain. DInEMMo is enabled with configurable smart contracts with the following features: (1) represent the ML model and use case attributes, (2) generation of models (new / enhanced) based on user input, (3) compute the price of the ML model based on the user policy, and (4) calculate the incentives to the model's owner and co-contributors. Using these features, we qualitatively evaluate the relevancy of the system for the use case on Medical Diagnostics and show the significance of domain specific properties in rewarding the contributors and further, determining the model price.