Welcome to the Pulitzer Center's
AI Spotlight Series
This toolkit builds on the Pulitzer Center’s AI Spotlight Series, an initiative designed to expand the field of AI accountability reporting by equipping journalists worldwide with the skills and knowledge necessary to cover AI critically and responsibly.
We have conducted more than two dozen webinars and in-person sessions since 2024 and have trained nearly 3,000 journalists across the globe in seven languages. In an effort to make the AI Spotlight Series resources even more accessible, we are open-sourcing the course modules, slide decks, and videos produced by our instructors who are some of the world’s leading tech reporters and editors.
We invite journalists to access, adapt, and build on a growing body of knowledge to strengthen AI accountability reporting worldwide. Once you have completed the curriculum, please share your feedback with us!
The core content of the AI Spotlight Series is divided into three tracks: one for reporters on any desk, one for reporters focused on covering AI or deepening their knowledge of AI reporting, and one for editors (on any desk) commissioning stories and thinking strategically about their team’s overall coverage.
Each course is designed to give you a strong grounding in what AI is and how it works as well as the tools to identify critical stories—from spot news to deep investigations—that will highlight the technology’s impacts, hold companies and governments accountable, and drive policy and community change, while avoiding both hype and unnecessary alarmism.
The AI Spotlight Series is funded with the support of the John D. and Catherine T. MacArthur Foundation, Notre Dame-IBM Technology Ethics Lab, Ford Foundation, and individual donors and foundations who support our work more broadly. Read our terms of use and privacy policy. For questions, please reach out to aispotlight@pulitzercenter.org.
Track 1
Introduction to AI Reporting
This track is designed for reporters with minimal or no knowledge of AI who are interested in getting started.
This track is designed for reporters with minimal or no knowledge of AI who are interested in getting started. Perhaps you are on the education beat, keen to dive into the way AI is entering the classroom; perhaps you are on the breaking news desk, being asked to write about the latest AI claims from Elon Musk. We will begin with the basics, covering the history of AI, how the technology works, and key technical concepts such as “neural networks” and “deep learning.” We will also dissect what makes a good AI accountability story, from quick turnaround stories to more ambitious investigations, and dig deeper into a few examples. At the end of the course, those who are interested in learning more are encouraged to continue on with the AI reporting intensive.
Through this course, participants will learn:
- background knowledge on the history of AI to understand its latest developments
- a clearer understanding of how AI works and how to better cover the multiple parts that make up its supply chain
- how to resist AI hype, and how to identify and cover the most important dangers, failures, and real world impacts of AI
- how to use their existing arsenal of tools to cover AI from every angle
Many of the lessons from Track 1 are included in Track 2 and 3 as well. Please note which lessons are repeated in the other tracks by clicking on the dropdown next to the track name titled "Included Lessons from Other Tracks."
Our AI Accountability Community
When you've finished these videos, continue learning in our AI accountability community.
Track 2
Reporting on AI Intensive
This track is designed for reporters who have a grasp of AI, spend a significant amount of their time covering technology, and want to go deeper.
This track is designed for reporters who have a grasp of AI, spend a significant amount of their time covering technology, and want to go deeper. It will be an opportunity to clarify your understanding of technical concepts and think more expansively about how to cover the different facets of this fast-moving story.
The modules in this track are organized in three parts. We will first cover the history of AI, the AI supply chain, and key technical concepts such as what it means to train a deep-learning model and the difference between predictive and generative AI. We will also dive into basic data literacy skills and methods for investigating AI bias. Then we will dig into what makes a good accountability story and how to report on governments and communities, including by documenting harms and embedding with affected populations. Finally, we will dive deeper into more technical concepts related to generative AI (think: transformers, diffusion models, scaling laws), and how to report on companies, including by cultivating inside sources.
Through this course, participants will learn:
- background knowledge on the history of AI to understand its latest developments
- a clearer understanding of how AI works and how to better cover the multiple parts that make up its supply chain
- how to resist AI hype, and how to identify and cover the most important dangers, failures, and real world impacts of AI
- practical reporting methods to report on AI, including basic spreadsheets, public records requests, and strategies on approaching sources working at tech companies
- techniques for investigating bias in automated systems and tracking misinformation
Our AI Accountability Community
When you've finished these videos, continue learning in our AI accountability community.
Track 3
An Editor's Guide to AI
This track is designed for managing editors, executive editors, desk editors, social media editors—anyone in charge of directing coverage, commissioning stories, or packaging and producing them for public consumption.
This track is designed for managing editors, executive editors, desk editors, social media editors—anyone in charge of directing coverage, commissioning stories, or packaging and producing them for public consumption. We will identify different types of AI stories and dissect what sets apart the best coverage, including its framing, headline, and artwork. You’ll learn how to assess both pitches and filed stories, and avoid common pitfalls that can mislead or confuse an audience (or an editor).
Through this course, participants will learn:
- background knowledge on the history and latest developments in AI that can help them identify, plan, and execute AI coverage and projects in the newsroom
- the different categories of AI stories and how to shape them
- how to resist AI hype, and how to identify and cover the most important dangers, failures, and real world impacts of AI
- how to spot and avoid AI clichés and jargon
- tips and tricks on how to assess and question AI pitches to make for better stories
- practical tools and guidelines that can help editors and their reporters better navigate coverage of AI and its impacts
Our AI Accountability Community
When you've finished these videos, continue learning in our AI accountability community.
AI Spotlight Coaches
Led by award-winning journalist Karen Hao, a dedicated group of journalists, editors, and Pulitzer Center staff collaboratively designed this open-source curriculum. Meet the five main contributors to the AI Spotlight Series below.
Karen Hao
Lead Designer, AI Spotlight Series
Reporter
Karen Hao is the New York Times bestselling author of EMPIRE OF AI and an award-winning reporter covering the impacts of artificial intelligence on society. She writes for publications including The Atlantic and was formerly a reporter for the Wall Street Journal, covering American and Chinese tech companies, and a senior editor for AI at MIT Technology Review. Her work is regularly taught in universities and cited by governments. She has received numerous accolades for her coverage, including an American Humanist Media Award, an American National Magazine Award for Journalists Under 30, and a TIME100 AI honor. She received her Bachelor of Science in mechanical engineering from MIT.
Lam Thuy Vo
Co-designer, AI Spotlight Series
Reporter, Documented
Lam Thuy Vo is a journalist who marries data analysis with on-the-ground reporting to examine how systems and policies affect individuals. She is currently an investigative reporter working with Documented, an independent, non-profit newsroom dedicated to reporting with and for immigrant communities, and an associate professor of data journalism at the Craig Newmark Graduate School of Journalism. Previously, she was a journalist at The Markup, BuzzFeed News, The Wall Street Journal, Al Jazeera America and NPR's Planet Money.
She has also worked as an educator, scholar, and public speaker for a decade, developing newsroom-wide training programs for institutions including Al Jazeera America and The Wall Street Journal; workshops for journalists across the U.S. as well as from Asia, Latin America, and Europe; and semester-long courses for the Craig Newmark Graduate School of Journalism. She's brought her research about misinformation and the impact of algorithms on our political views to Harvard, Georgetown, MIT, Columbia, Data & Society, and other institutions. In 2019, she published a book about her empirical approach to finding stories in data from the Internet for No Starch Press.
Gabriel Sean Geiger
Co-designer, AI Spotlight Series
Investigative Reporter, Lighthouse Reports
Gabriel Geiger is an Athens-based investigative journalist specializing in surveillance and algorithmic accountability reporting. He is currently an investigative journalist at Lighthouse Reports. His work has appeared in WIRED, Le Monde, Der Spiegel and The Guardian, among others.
Gideon Lichfield
Co-designer, AI Spotlight Series
Editor
Gideon Lichfield began his career as a science and technology writer at The Economist. He then took foreign postings in Mexico City, Moscow, and Jerusalem before moving to New York City in 2009. He was one of the founding editors at Quartz, then editor-in-chief of MIT Technology Review and, most recently, of WIRED. While at MIT, he also edited post-pandemic speculative fiction for MIT Press. He is based in the San Francisco Bay Area.
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Tom Simonite
Co-designer, AI Spotlight Series
Tech Companies Editor, Washington Post
Tom Simonite edits technology coverage for The Washington Post from San Francisco. He was previously a senior editor at WIRED and spent six years reporting on artificial intelligence. He has also written and edited technology coverage while on staff at MIT Technology Review and New Scientist magazine in London.
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