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

## TL;DR

This paper explores a data market framework that assesses intrinsic and extrinsic data values, providing valuation methods for data in various contexts to facilitate equitable AI development.

## Contribution

It introduces a comprehensive data valuation approach considering relevance, uniqueness, and market dynamics, enhancing data trading and governance for AI.

## Key findings

- Proposes valuation techniques based on relevance and uniqueness.
- Defines intrinsic and extrinsic data value metrics.
- Offers methods for absolute, relative, and conditional data valuation.

## Abstract

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).