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How we used AI to help us find lobbyists at COP [1]
['Nicu Calcea', 'Senior Data Investigator']
Date: 2025-07
Every year, Global Witness works with partners in the Kick Big Polluters Out coalition to identify fossil fuel lobbyists who are attending the COP climate talks. For COP29, we pioneered an AI-assisted approach to the process that streamlined and strengthened our analysis
Every year, as part of the Kick Big Polluters Out (KBPO) coalition, Global Witness helps identify fossil fuel lobbyists who register to attend the COP climate talks.
These are well-attended events – 52,305 people signed up for COP29 in Azerbaijan, and 81,027 registered the year before.
The UN Climate Change Conference publishes the list of attendees and their organisational affiliations a few days into the event. Classifying individual attendees would be time-consuming and unnecessary. Instead, we focused on classifying organisations that might include multiple attendees.
That still leaves us with about 20,000 entries in the past year, each of which must be manually researched and classified by a KBPO volunteer – a laborious and time-consuming ordeal.
To speed this process up, we have trialled the use of generative AI to do some pre-classification for us.
The accuracy problem
By default, Large Language Models (LLMs), like the ones that power ChatGPT, have a problem with the truth.
When prompted with a question, particularly one that refers to a very specific and not well-known topic, an LLM can "hallucinate" – producing a plausible-sounding but completely fabricated answer.
For example, ask Claude (a ChatGPT competitor) to tell you about a company like "Zefiro Methane", which has sent three delegates to COP29, and it will say it could be a "research initiative focused on studying methane emissions." The company, in fact, specialises in methane abatement.
The AI even warns that it "might hallucinate details" as it "does not have concrete, verifiable information about Zefiro Methane."
To mitigate this issue, many LLMs have been paired with search engines.
If you ask ChatGPT to tell you about Zefiro Methane, it searches the web, reads a few of the resulting pages, and then returns a much more accurate response.
This approach, sometimes called Retrieval Augmented Generation (RAG) or grounding, works by attaching relevant texts from company websites, news articles or other sources to the question that the AI must answer. This tends to produce more accurate results.
Classifying at scale
To do this at scale, we developed a tool to search the internet for each of the 20,000 organisations registered to attend COP29, collect the results, and then capture relevant text from those results.
One challenge with this approach is that many websites use CAPTCHAs or other methods to prevent bots from scraping their pages.
To increase our chances of getting as much data as possible, we routed our requests through Oxylabs's Project 4beta, which significantly reduced these roadblocks.
The resulting texts were then combined and sent to one of OpenAI's generative AI models (gpt-4o-mini) along with a description of what we consider a fossil fuel lobbyist. The AI model then used that information to tell us whether it thought the organisation is a fossil fuel lobbyist, along with an explanation and sources.
Because AI models cannot be fully trusted to produce accurate results, KBPO volunteers still classified each organisation by hand. Each positive classification was then fact-checked for a second time by another volunteer, as we were especially concerned about the risk of accusing a company of being fossil fuel-affiliated.
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[1] Url:
https://globalwitness.org/en/campaigns/fossil-fuels/how-we-used-ai-to-help-us-find-lobbyists-at-cop/
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