ViSDM: A Liquid Democracy based Visual Data Marketplace for Sovereign Crowdsourcing Data Collection

Venkata Satya Sai Ajay Daliparthi, Nurul Momen, K. Tutschku, Miguel de Prado
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引用次数: 2

Abstract

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.
ViSDM:基于流动民主的可视化数据市场,用于主权众包数据收集
训练数据集的大小和多样性直接影响人工智能模型的决策过程。因此,需要大量和多样化的数据集来增强人工智能应用程序的部署过程。众包市场为费力的数据收集过程提供了一种快速可靠的替代方案。然而,现有的众包市场要么是中心化的,要么没有完全提供数据主权。相比之下,这项工作提出了一个分散的众包平台,通过原型实现以及商业实体的积极参与,赋予用户对其收集的数据的主权,称为视觉主权数据市场(ViSDM)。这项工作通过引入(i)基于流动民主的投票系统来协商买方和多个数据所有者之间的价格,(ii)基于人工智能的自动化每样本值计算功能来评估数据并在数据所有者之间分配利润,从而为数据市场格局做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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