Data Markets to support AI for All: Pricing, Valuation and Governance
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Abstract (arXiv)
We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we explain how to perform valuation of data in absolute terms (i.e just by itself), or relatively (i.e in comparison to multiple datasets) or in conditional terms (i.e valuating new data given currently existing data).
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 May 2019.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 3; 2025: 0; 2026: 1.
6 papers.
- 2022Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular datacited by 22
- 2022Fundamentals of Task-Agnostic Data Valuationcited by 19
- 2026A Sustainable AI Economy Needs Data Deals That Work for Generatorscited by 3
- 2024Data Measurements for Decentralized Data Marketscited by 2
- 2024Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuationcited by 2
- 2024LossVal: Efficient Data Valuation for Neural Networkscited by 1
- 2026A Sustainable AI Economy Needs Data Deals That Work for Generators15 Jan 2026
- 2024LossVal: Efficient Data Valuation for Neural Networks5 Dec 2024
- 2024Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation22 Aug 2024
- 2024Data Measurements for Decentralized Data Markets6 Jun 2024
- 2022Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data24 Oct 2022
- 2022Fundamentals of Task-Agnostic Data Valuation25 Aug 2022