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.