You find yourself staring at a dataset with tens or hundreds of thousands of rows. Maybe you want to get up-to-date FOIA contact details for all government departments in your country, or to find out which political donors have links to the fossil fuels industry. What do you do?
Large Language Models (LLMs) can help journalists automate simple research and classification tasks that would take an unreasonably long time to do manually.
In this session, we'll outline how Global Witness has used LLMs, search engines and web scraping to help us identify fossil fuel lobbyists at COP29. These techniques can be applied to other investigations and research tasks.
The workshop will cover:
- An interactive classification demo - Some basic tips on setting up a research/classification project - The challenges of doing AI research at scale and how to address them - Using more advanced tools
After attending this session, you will be able to take an existing dataset and automatically augment it with new data, opening up the potential for new stories and investigations.
If you want to follow along with the classification demo, you'll need to be able to run Jupyter Notebooks on your device or have a Google account. A basic understanding of Python would be useful, but we won't be writing any new code.
