The size and diversity of the training datasets directly influences the decision-making process of AI models. Therefore, there is an immense need for massive and diverse datasets to enhance the deployment process of AI applications. Crowdsourcing marketplaces provide a fast and reliable alternative to the laborious data collection process. However, the existing crowdsourcing marketplaces are either centralized or do not fully provide data sovereignty. By contrast, this work proposes a decentralized crowdsourcing platform through prototypical implementation along with active involvement of business entities, that grants the users sovereignty over their collected data, named as Vision-Sovereignty Data Marketplace (ViSDM). This work contributes to the data marketplaces landscape by introducing (i) A liquid democracy-based voting system to negotiate prices between a buyer and multiple data owners, (ii) An automated AI-Based per-sample value calculation function to evaluate the data and distribute profit among the data owners. © 2023 Owner/Author.

The demand for large-scale diverse datasets is rapidly increasing due to the advancements in AI services impacting day-to-day life. However, gathering such massive datasets still remains a critical challenge in the AI service engineering pipeline, especially in the computer vision domain where labeled data is scarce. Rather than isolated data collection, crowdsourcing techniques have shown promising potential to achieve the data collection task in a time and cost-efficient manner. In the existing crowdsourcing marketplaces, the crowd works to fulfill consumer-defined requirements where in the end consumer gains the data ownership and the crowd is compensated with task-based payment. On the contrary, this work proposes a blockchain-based decentralized marketplace named Vision Sovereignty Data Marketplace (ViSDM), in which the crowd works to fulfill global requirements & holds data ownership, the consumers pay a certain data price to perform a computing task (model training/testing), the data price is distributed among the crowd in a one-to-many manner through smart contracts, thus allowing the crowd to gain profit from each consumer transaction occurring on their data. The marketplace is implemented as multiple smart contracts and is evaluated based on blockchain-transaction gas fees for the stakeholder interaction & by running scenarios-based simulations. Furthermore, discussions address the challenges included in maintaining data quality and the future milestones towards deployment. © 2023 Owner/Author.

The AI marketplace ecosystem accelerates multiple modules of the AI engineering pipeline by fostering collaboration between stakeholders. However, marketplace collaborators often face a dilemma in striking a balance between sharing artifacts and protecting intellectual property (IP) rights. Thus, there is a need for a license management system within the AI marketplace to facilitate the exchange of artifacts in a trusted and secure manner. 

This work shares experiences while building such a license management system within the Bonseyes marketplace (BMP), a functional crowdsourcing AI marketplace that specializes in deploying real-time applications on edge devices. The BMP was developed, and its applicability is proven through the European H2020 project by a series of open calls and workshops, for gathering stakeholders and orchestrating the marketplace operations. 

The main contributions of this work are (i) implementation of an end-to-end license management system that deals with selecting license templates, license agreement interaction between seller and buyer, and the generation and enforcement of human- and machine-readable license files, and (ii) introduction of "Synchronization licenses'' concept from the music industry to the AI marketplace context where consumers acquire a license to integrate the artifact into another application, and a respective BMP use-case for collaborative AI engineering.