Pulitzer Center Update March 23, 2022
10 Takeaways from Journalists at the Forefront of AI Reporting
On February 24, 2022, Pulitzer Center grantees Karen Hao and Joanne Cavanaugh Simpson joined Reuters Executive Editor Gina Chua for a conversation on AI accountability. The speakers shared how they got started reporting on algorithms, discussed their challenges and breakthroughs, and offered tips for colleagues interested in covering this urgent, underreported story.
Here are 10 key takeaways from the conversation. You can watch the full conversation here.
- AI has become a sort of digital infrastructure like the internet, and when something becomes infrastructure, it becomes invisible. AI is absolutely a global story, because AI crosses borders.
- There are many ways you could cut into covering AI. You can abstract it to the level of something being automated. Should it be automated? How is that automation potentially causing harm?
- To frame AI stories, ask yourself: Where’s the harm? What’s the greater harm? Are there things that are being automated in the industry that you cover?
- Be curious and pay attention to the ways AI is infiltrating your life.
- If you notice your flight arrival time was changed from 8:00pm to 2:00am, you can start looking up how airlines are using AI. Are they using it to save money? Are they using it to reduce routes to smaller towns, basically creating equity issues?
- If you read a story about a group of moms stopping a crypto-mine from building next to a school because of noise pollution, find out how much of a problem it is. What are the impacts of this? Who is being impacted?
- As a journalist, follow your instincts: Is something bothering you? Does something not seem right?
- (Hao) These are some simple questions you can start asking:
- Has somebody acquired an algorithm here?
- Is that built into a process whether you’re covering the IRS, or the local police department, or a company you know?
- Did a machine substitute a human at some point?
- What is the machine?
- Who are the other players?
- (Hao) These are some simple questions you can start asking:
- Read books, industry reports, and research papers. Go to AI research conferences, meet people, and call up researchers and ask them to explain their research to you. (See below to take a look at the list of resource speakers and the audience shared during the event.)
- You want to go in with an open mind, even with something that has so many potential problems as AI. Any new technology rolls out faster than we know what to do with it, so the harm is almost endemic, to some degree. But it doesn’t always turn out that way.
- (Cavanaugh Simpson) One way I approach stories and sources that might be worrying about me writing is, “I want to know what you see as the benefits and the challenges.” I did that a lot with the police in particular, and some of them really wouldn’t want to talk about it, but then a couple of them did, and that was important for me to see what they saw as the benefits and challenges. Because that’s a reality, it’s their reality.
- Getting it right is a big challenge. The challenge is deeply understanding that manifestation of the technology, because it’s different every time. So you have to understand and double-check it. Make sure you’re describing it accurately.
- Bulletproof your story, fact-check it, and then send a no-surprises email to make sure that you have all your bases covered. So when the pushback happens, you are 100% confident that their pushback is not going to actually undermine the accuracy of your story, even if they try to attack you as an individual.
- Every country has local reporters working in their own communities. They know what the issues are locally. That’s why it’s so important to support work in international settings from local reporters, because there’s just so much going on that we need to know about.
Resources shared by speakers and audiences
- Pulitzer Center’s AI Accountability Network
- Call for applications for the AI Accountability Fellowships is now open. (Deadline: April 15, 2022)
- Machine Learning Grants (reviewed on a rolling basis)
- Sign up to receive further updates about our AI initiatives.
- AI stories, books, and resources mentioned by speakers
- Karen Hao’s AI flowchart
- "Prying Eyes" | Pulitzer Center: by Cavanaugh Simpson
- Impactful work on spy planes in Baltimore
- Hello World: Being Human in the Age of Algorithms
- arXiv: Open-access archive of research papers
- This CEO's AI-Powered Garbage Cans Help Reduce Food Waste | TIME
- "Inside Facebook's African Sweatshop" | TIME
- "IRS abandons facial recognition plan after firestorm of criticism"
- On Cavanaugh Simpson’s reading/following list:
- Books
- Outlets that cover AI
- Axios Future
- WIRED
- Intercept
- Coda
- Hao’s podcast, In Machines We Trust
- Organizations that offer training and other resources
- Investigative Reporters & Editors (IRE)
- Society of Professional Journalists (SPJ)
- National Institute for Computer-Assisted Reporting (NICAR)
- Book recommendations from the audience
- Online resources
- Algorithm Tips, a site with many resources for learning about covering AI, curated by Nicholas Diakopoulos.
- Database | Algorithm Tips, a database of leads for algorithms in use in the government.
- JournalismAI’s training on AI and ML for Journalists
- AI Journalism Starter Pack from JournalismAI
- AI For Everyone course on DeepLearning.AI
- AI Ethicist
- Conferences, tips, and making connections
- European Data and Computational Journalism Conference
- “I am in the graduate program in Criminal justice at UC Irvine, with a professional background in Data analytics and AI. Please feel free to reach out for any of your journalistic needs” (shared by Pramod Kunju/[email protected])
Please feel free to reach out to me at [email protected] if you have any questions regarding the AI Accountability Fellowships (call for proposals open until April 15) and Machine Learning Grants.
