世界经济论坛-推进数据公平：一个面向行动的框架（英）

Advancing Data Equity: An Action-Oriented FrameworkW H I T E P A P E RS E P T E M B E R 2 0 2 4Images: Getty Images, MidjourneyDisclaimer 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 the 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.© 2024 World Economic Forum. World Economic Forum reports may be republished in accordance with the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.ContentsPreface 3Executive summary 41 Defining data equity 52 Implementing data equity 73 Case studies 16Climate data collection and monitoring 17Women’s access to financial services in emerging markets 18Racial bias in healthcare 19Improving how the City of Boston collects gender data 20Mapping Indigenous territories 21National Statistical Offices as data stewards 224 Recommendations for stakeholders 23Conclusion 25Contributors 26Endnotes 28Look out for this icon for pages that can be interacted withThis report is interactiveTo ensure interactive capability, please download and open this PDF with Adobe Acrobat.PrefaceIn 2023, as generative artificial intelligence (genAI) and other technologies expanded their adoption and impact on society, the World Economic Forum established a Global Future Council focusing on the issue of data equity.1 Through research and discussions with experts in technology, data, business and social science, it became clear that a foundational definition and approach needed to be created to allow organizations of all types to build more equitable systems, processes, practices and outcomes. Our initial thoughts on this topic were published in our first white paper titled “Data Equity: Foundational Concepts for Generative AI”.2As our work evolved, it became clear that our data-driven world was not created in a manner that drives equitable outcomes, simply because it was not designed with equity in mind. It was created with all of our societal varieties, historical inequities, biases and differences. While we want these differences to be reflected in our technological solutions, we do not want those differences perpetuated, amplified or extended in our technology solutions. We want technology to create a better and more inclusive future, one where we solve problems, not repeat past ones. Our research and consultations revealed that data equity impacts diverse sectors, industries and regions. This complexity necessitates a flexible approach. In response, we developed a framework for responsible data practices that adapts to specific contexts while ensuring consistency and compliance with global regulations.The present white paper provides the glob