[论文解读] A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI

Hannah Chafetz, Sampriti Saxena+1 more|arXiv (Cornell University)|May 7, 2024

Big Data and Business Intelligence被引用 6

一句话总结

本文提出一个 Spectrum of Scenarios 框架，用以映射开放数据与生成式AI的交叉点，勾勒从与开放数据就绪相关的数据场景到开放式探索的情景，并指出推进数据质量与治理的五个关键领域。

ABSTRACT

Since late 2022, generative AI has taken the world by storm, with widespread use of tools including ChatGPT, Gemini, and Claude. Generative AI and large language model (LLM) applications are transforming how individuals find and access data and knowledge. However, the intricate relationship between open data and generative AI, and the vast potential it holds for driving innovation in this field remain underexplored areas. This white paper seeks to unpack the relationship between open data and generative AI and explore possible components of a new Fourth Wave of Open Data: Is open data becoming AI ready? Is open data moving towards a data commons approach? Is generative AI making open data more conversational? Will generative AI improve open data quality and provenance? Towards this end, we provide a new Spectrum of Scenarios framework. This framework outlines a range of scenarios in which open data and generative AI could intersect and what is required from a data quality and provenance perspective to make open data ready for those specific scenarios. These scenarios include: pertaining, adaptation, inference and insight generation, data augmentation, and open-ended exploration. Through this process, we found that in order for data holders to embrace generative AI to improve open data access and develop greater insights from open data, they first must make progress around five key areas: enhance transparency and documentation, uphold quality and integrity, promote interoperability and standards, improve accessibility and useability, and address ethical considerations.

研究动机与目标

• 推动在快速发展的 AI 生态中探索开放数据如何与生成式 AI 交互。
• 提出 Spectrum of Scenarios 框架，用以对开放数据与生成式 AI 的可能交叉进行分类。
• 识别每个情景的数据质量、溯源和治理先决条件。
• 强调组织和伦理考量，帮助数据拥有者拥抱 AI 驱动的开放数据访问与洞察。

提出的方法

• 开发定性框架（Spectrum of Scenarios）以映射开放数据与生成式 AI 的交叉点。
• 定义并分类情景：相关、适应、推理与洞察生成、数据增强，以及开放式探索。
• 在框架内分析每个情景的数据质量与溯源要求。
• 综合需要改进的领域，包括开放性（透明度、文档化）、质量、互操作性、可获取性和伦理等方面。

实验结果

研究问题

• RQ1 开放数据与生成式 AI 在实际中可能以何种方式交叉？
• RQ2 为支持每个交叉情景，需要哪些数据质量与溯源要求？
• RQ3 为实现 AI 驱动的开放数据访问与洞察，需要哪些组织实践与伦理考量？

主要发现

• Spectrum of Scenarios 框架概述了五种交叉类别：相关、适应、推理与洞察生成、数据增强，以及开放式探索。
• 数据拥有者利用生成式 AI 的进展取决于提高透明度和文档化。
• 要实现 AI 驱动的开放数据收益，需要在数据质量与完整性、互操作性与标准、可获取性与可用性，以及伦理考量方面进行改进。
• 本文主张通过结构化框架来推进开放数据就绪，而非临时性的 AI 采纳。