1、Data Equity:Foundational Concepts for Generative AIB R I E F I N G P A P E RO C T O B E R 2 0 2 3Images:Getty Images 2023 World Economic Forum.World Economic Forum reports may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public Lice

2、nse,and in accordance with our Terms of Use.Disclaimer This document is published by the World Economic Forum as a contribution to a project,insight area or interaction.The findings,interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by t

3、he World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum,nor the entirety of its Members,Partners or other stakeholders.ContentsIntroduction 1 Classes of data equity2 Data equity across the data lifecycle 3 Data equity challenges in foundation mode

4、ls 4 Focus areas for key stakeholders5 DiscussionConclusionContributorsEndnotes 34691114151618Data Equity:Foundational Concepts for Generative AI2IntroductionOver the past several months,a series of technological advances have emerged as a result of generative artificial intelligence(genAI)tools,inc

5、luding ChatGPT,Bard,Midjourney,and Stable Diffusion.The use of these tools has gained significant attention and captured the imagination of public and industry stakeholders due to its capabilities,wide range of applications and ease of use.Given its potential to challenge established business practi

6、ces and operational paradigms,and the promise of rapid innovation coupled with the likelihood of significant disruption,genAI is sparking global conversations.These anticipated,far-reaching consequences have a societal dimension and will require comprehensive engagement from key stakeholders such as