A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI
<|reference_start|>A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI: 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.<|reference_end|>
@article{chafetz2024a,
    title={A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open
  Data and Generative AI},
    author={Hannah Chafetz, Sampriti Saxena, and Stefaan G. Verhulst},
    journal={arXiv preprint arXiv:2405.04333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04333},
    primaryClass={cs.AI}
}