Process
Status Items Output None Questions None Claims None Highlights Done See section below
Highlights
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he Times programmed an artificial-intelligence tool to analyze satellite imagery of South Gaza to search for bomb craters. The AI tool detected over 1,600 possible craters. We manually reviewed each one to weed out the false, like shadows, water towers, or bomb craters from a previous conflict. We measured the remaining craters to find ones that spanned roughly 40 feet across or more, which experts say are typically formed only by 2,000-pound bombs. Ultimately, we identified 208 of these craters in satellite imagery and drone footage, indicating 2,000-pound bombs posed a pervasive threat to civilians seeking safety across South Gaza. That part of the work was led by Ishaan Jhaveri. It’s a great example of a story that simply could not have otherwise been told without machine learning paired with journalists and experts.
✏️ One takeaway for me here is about how AI tools feel best used… as tools. They do the grunt work, and humans validate, double check, analyze and output the result. We don’t just take the word of the AI as gospel. It needs to be verified. AI is a tool, and can be well-utilized in that fashion, if we just bothered to remember that they shouldn’t replace human verification. Hell, we generally don’t (or shouldn’t) take humans at their own words.. proper process and critical thinking means to question and verify whatever you read or consume. Why should AI get a pass? 🔗 View Highlight
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So we’ve seen how ML models are at their best, for journalism, when recognizing patterns the human eye alone can’t see. Patterns in text, data, images of data, photos on the ground, and photos taken from the sky.
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Of course, a lot of this has been possible for many years. But there are some equally inspiring uses of the hottest new machine-learning technology, LLMs, or generative AI. If traditional machine learning is good at finding patterns in a mess of data, you might say that generative AI’s superpower is creating patterns
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The Marshall Project, a non-profit newsroom covering the U.S. justice system, has been investigating what books are banned in state prisons and why. It maintains a database of the actual books, but also got its hands on the official policies that guide book banning in 30 state prison systems. These are often long and esoteric documents, and The Marshall Project wanted to make the policies more accessible to interested readers. Its journalists went through each document to identify the parts that actually mattered, and then Andrew Rodriguez Calderón, using OpenAI’s GPT-4, employed a series of very specific prompts to generate useful, readable summaries of the policies
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Those summaries were then reviewed again by journalists before publication
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decoding bureaucratic documents with an LLM. He built a custom GPT trained to summarize audit reports of government agencies in the Philippines. These reports often turn up evidence of corruption, but they are very long and hard to read. Now, several Filipino journalists are making use of Jaemark’s tool to identify graft and find promising new lines of reporting.
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It’s called Realtime, a news site, by Matthew Conlen and Harsha Panduranga, that charts regularly updated feeds of data from financial markets, sports, government records, prediction markets, public-opinion polls, etc. The data and charts are fully automated. The site tries to highlight charts that are showing interesting data, like an outlier, or particularly strong growth or decline. So far, cool but basic math.
Where LLMs play a role is in providing context: the headlines and other brief copy that appear around the charts to help readers understand what they’re looking at. The air quality in New York today is good, subway ridership is still below pre-pandemic levels, but on the bright side, our fight against rats is showing progress.
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People look at tools like ChatGPT and think their greatest trick is writing for you. But, in fact, the most powerful use case for LLMs is the opposite: creating structure out of unstructured prose.
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LLMs are useful tools for summarizing text, fetching information, understanding data, and creating structure.
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But always, always with human oversight: guiding the summaries and then checking the results, teaching the bot to navigate an audit report, deciding when and when not to put the bot in control, and designing the rest of the software that powers the site. In all of these cases, it’s humans first and humans last, with a little bit of powerful, generative AI in the middle to make the difference.
✏️ Human oversight always 🔗 View Highlight