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Data Innovation

As every sector of the global economy and nearly every facet of modern society undergo digital transformation, ITIF advocates for policies that spur not just the development of IT innovations, but more importantly their adoption and use throughout the economy. ITIF’s Center for Data Innovation formulates and promotes pragmatic public policies designed to maximize the benefits of data-driven innovation in the public and private sectors.

Ayesha Bhatti
Ayesha Bhatti

Head of Digital Policy, UK & EU

Center for Data Innovation

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Daniel Castro
Daniel Castro

Vice President and Director, Center for Data Innovation

Information Technology and Innovation Foundation

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Eli Clemens
Eli Clemens

Policy Analyst

Center for Data Innovation

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Martin Makaryan
Martin Makaryan

Research Assistant, Digital Policy

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Hodan Omaar
Hodan Omaar

Senior Policy Manager

Center for Data Innovation

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Yanzi Xu
Yanzi Xu

Research Fellow

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Featured

Picking the Right Policy Solutions for AI Concerns

Picking the Right Policy Solutions for AI Concerns

Some concerns are legitimate, but others are not. Some require immediate regulatory responses, but many do not. And a few require regulations addressing AI specifically, but most do not.

Exploring Data-Sharing Models to Maximize Benefits From Data

Exploring Data-Sharing Models to Maximize Benefits From Data

Data-driven innovation has the potential to be a massive force for progress. Data sharing enables organizations to increase the utility and value of the data they control and gain access to additional data controlled by others.

Overcoming Barriers to Data Sharing in the United States

Overcoming Barriers to Data Sharing in the United States

Without policy change, the United States will continue trending toward data siloes—an inefficient world in which data is isolated, and its benefits are restricted.

Digital Equity 2.0: How to Close the Data Divide

Digital Equity 2.0: How to Close  the Data Divide

Unlike the digital divide, many ignore the data divide or argue that the way to close it is to collect vastly less data. But without substantial efforts to increase data representation and access, certain individuals and communities will be left behind in an increasingly data-driven world.

More Publications and Events

March 3, 2025|Events

Tech Policy 202: Spring 2025 Educational Seminar Series for Congressional Staff

ITIF’s spring seminar course explores core emerging technologies and issues that are reshaping our world and, in the process, creating public policy challenges and opportunities. The course is open to congressional staff only.

January 17, 2025|Blogs

It’s Time the US Speaks With One Voice on AI

The United States has seen fragmented efforts in AI regulation. Lacking coordination, these efforts failed to create a unified strategy, leaving the U.S. without a clear voice on AI.

January 10, 2025|Blogs

Moonshot AI: Betting Big on Long-Context, Confronting the Challenges of Scale and Reliability

This post is part of our ongoing series on China’s AI unicorns. Moonshot AI, a Beijing-based startup, rose quickly in China's AI market with its flagship chatbot Kimi, known for processing 2 million characters and a consumer-focused approach.

January 5, 2025|Blogs

How DOJ’s Proposal to Break Up Google Would Hurt U.S. Competitiveness in AI

Last October, the U.S. Department of Justice (DOJ) proposed a sweeping set of remedies in response to an earlier court ruling that Google violated antitrust laws with its search business. While most attention has focused on the potential partial breakup of Google—the DOJ has proposed the divesture of the Chrome web browser and the Android mobile operating system—the proposed remedies would also have significant implications for U.S. competitiveness in AI.

December 16, 2024|Reports & Briefings

Why AI-Generated Content Labeling Mandates Fall Short

Mandatory labeling for AI-generated content, particularly through watermarking, is neither a reasonable nor effective solution to the issues policymakers seek to address. Rather than singling out AI-generated content, policymakers should prioritize building trust within the digital ecosystem as a whole.

December 12, 2024|Blogs

Zhipu AI: China’s Generative Trailblazer Grappling with Rising Competition

This post is part of our ongoing series on China’s AI unicorns. Zhipu AI, China’s largest AI start-up by workforce, develops bilingual LLMs like GLM while advancing open-source and commercial AI innovation.

December 12, 2024|Blogs

China’s AI Unicorns: Exploring the Five Startups Vying to Rival Western Counterparts

Five Chinese generative AI start-ups, known as "AI Tigers," have achieved unicorn status by 2025, highlighting China's rapid innovation and distinct strategies in the global AI race. This blog series delves into each of these five startups.

December 2, 2024|Blogs

The New UK Data Bill Is Good but It Could Be Much Better

The UK’s new Data (Use and Access) Bill aims to enable AI and data-driven services, reviving parts of the previous DPDI Bill. To seize post-EU opportunities, it should adopt provisions tightening personal data definitions and aligning government-ICO priorities.

November 25, 2024|Reports & Briefings

Digital Transformation Should Be at the Heart of the UK’s Economic Agenda

The UK stands at a critical moment when embracing digital transformation, AI, and data innovation is not just an opportunity but also a necessity. By implementing forward-thinking policies, the UK can not only drive economic growth but also position itself as a global leader in emerging technologies.

November 25, 2024|Blogs

Denying Copyright for AI-Assisted Art Threatens Innovation

Jason M. Allen, an artist whose AI-generated image won a digital art competition prize in 2022, recently sued the U.S. Copyright Office for rejecting his application for copyright of the image. In its refusal to grant copyright protection to Allen’s work—which he created using 624 prompts on the generative AI platform Midjourney—the Copyright Office argued that the artist’s creative process to generate the award-winning image did not meet the criteria for “human authorship as we understand it.”

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