# Data Markets to support AI for All: Pricing, Valuation and Governance

arXiv: https://arxiv.org/abs/1905.06462 · HTML: https://mlsanity.com/paper/1905.06462/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 6 of 6)

- [Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data](https://mlsanity.com/paper/2210.13043/) (2022)
- [Fundamentals of Task-Agnostic Data Valuation](https://mlsanity.com/paper/2208.12354/) (2022)
- [A Sustainable AI Economy Needs Data Deals That Work for Generators](https://mlsanity.com/paper/2601.09966/) (2026)
- [Data Measurements for Decentralized Data Markets](https://mlsanity.com/paper/2406.04257/) (2024)
- [Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation](https://mlsanity.com/paper/2408.12659/) (2024)
- [LossVal: Efficient Data Valuation for Neural Networks](https://mlsanity.com/paper/2412.04158/) (2024)

## 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).