# Marketplace for AI Models

arXiv: https://arxiv.org/abs/2003.01593 · HTML: https://mlsanity.com/paper/2003.01593/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs](https://mlsanity.com/paper/2105.04404/) (2021)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [A Review on Building Blocks of Decentralized Artificial Intelligence](https://mlsanity.com/paper/2402.02885/) (2024)
- [Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation](https://mlsanity.com/paper/2403.01417/) (2024)
- [Privacy-Preserving Dataset Combination](https://mlsanity.com/paper/2502.05765/) (2025)
- [Decentralized Technologies for AI Hubs](https://mlsanity.com/paper/2306.04274/) (2023)

## Abstract (arXiv)

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.