Fact Checking Climate Claims With Open Source Models

Climate+Tech FactChecker takes a major step toward democratizing climate fact-checking by integrating Ollama, enabling deployment of powerful open source language models for transparent and customizable verification of climate claims. We’re excited to announce that Climate+Tech FactChecker now fully integrates with Ollama, enabling deployment of powerful open source language models for climate fact-checking. This integration marks a significant step toward more transparent, customizable, and accessible climate information verification. The transition to open source AI models for fact-checking addresses several critical challenges in the fight against climate misinformation: Transparency and Trust: Recent studies show that 68% of people are concerned about the “black box” nature of AI systems1. Open source models allow complete visibility into the decision-making process, crucial for building trust in automated fact-checking.

Independence: With 73% of experts emphasizing the importance of independent verification systems2, open source models ensure fact-checking isn’t dependent on any single commercial entity or authority. npj Climate Action volume 4, Article number: 17 (2025) Cite this article Accurate identification of true versus false climate information in the digital age is critical. Misinformation can significantly affect public understanding and policymaking. Automated fact-checking seeks to validate claims against trustworthy factual data. This study tackles the challenge of fact-checking climate claims by leveraging the currently most capable Large Language Models (LLMs).

To this end, we introduce Climinator, an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning. It significantly boosts the performance of automated fact-checking by integrating authoritative, up-to-date sources within a novel debating framework. This framework provides a trustworthy and context-aware analysis incorporating multiple scientific viewpoints. Climinator helps identify misinformation in real time and facilitates informed dialog on climate change, highlighting AI’s role in environmental discussions and policy with reliable data. In the era of digital information abundance, the endeavor to counter climate misinformation has found a promising ally in artificial intelligence (AI). Research shows that engaging with an AI chatbot on climate change can significantly align public perception with scientific consensus1, highlighting the importance of ensuring that the large language models (LLMs) underpinning these systems are...

Therefore, we ask how well we can embed scientific consensus into automated fact-checking. To this end, we developed Climinator—an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning. Climinator evaluates the veracity of climate statements and improves its verdicts with evidence-based and scientifically credible reasoning and references to relevant literature. Our vision is to use AI to catalyze a well-informed global climate dialog, enrich public discourse with scientific insights, and foster a more informed society ready to engage with climate challenges. Climinator serves as a first step in this direction. Platforms like Climate Feedback and Skeptical Science have made commendable efforts to involve climate scientists in volunteering their expertise and providing an essential service in addressing climate misinformation.

These scientists voluntarily dedicate their time to giving concise science-based evaluations, including references, and delivering a final verdict on disputed claims. Despite their valuable contributions, these efforts face significant challenges, including scalability and actuality. Hence, their impact is limited by the sheer volume of misinformation and skepticism in digital media, worsened by misinformation spreading more rapidly and widely than factual information2. As a response, automated fact-checking3,4 aims to debunk misinformation at scale using natural language processing methods. While automated fact-checking tools have improved, they struggle with complex claims due to a lack of detailed reasoning5,6,7, particularly in the domain of climate change8. To address this problem, we introduce an advanced framework that overcomes these limitations by integrating LLMs within a Mediator-Advocate model.

Although recent work has explored the aggregation of different viewpoints using LLMs to build a general consensus9, we address real-world claim complexities and evidence controversies in a novel way10,11,12. In particular, we introduce separate “Advocates,” each drawing on a distinct text corpus to represent a specific viewpoint, while a “Mediator” either asks follow-up questions or synthesizes these perspectives into a cohesive and balanced... Accurate identification of true versus false climate information in the digital age is critical. Misinformation can significantly affect public understanding and policymaking. Automated fact-checking seeks to validate claims against trustworthy factual data. This study tackles the challenge of fact-checking climate claims by leveraging the currently most capable Large Language Models (LLMs).

To this end, we introduce Climinator, an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning. It significantly boosts the performance of automated fact-checking by integrating authoritative, up-to-date sources within a novel debating framework. This framework provides a trustworthy and context-aware analysis incorporating multiple scientific viewpoints. Climinator helps identify misinformation in real time and facilitates informed dialog on climate change, highlighting AI's role in environmental discussions and policy with reliable data. Keywords: Climate sciences; Communication; Education; Environmental social sciences. Competing interestsThe authors declare no competing interests.

Fig. 1. Performance in terms of accuracy… Fig. 1. Performance in terms of accuracy (averaged micro-F1) of various models in classifying Climate…

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People often make Claims about it, but not all statements are true. This paper talks about a new tool designed to help check if these claims are accurate. The tool uses advanced computer models that can understand and analyze large amounts of information quickly. In today's world, people are surrounded by information, some of which may not be true. This is especially true with climate change, where various individuals and groups make conflicting statements that can confuse the public. The recent claims made by notable figures can spark debate about what is accurate and what is not.

For example, a recent claim stated that there is no scientific Evidence supporting the need to phase out fossil fuels to limit global warming. This claim shows how critical it is to have a reliable system that can verify such statements. The tool discussed in this paper is designed to automatically check facts related to climate change. It uses a method called the Mediator-Advocate framework. This framework helps the tool to collect and evaluate different scientific opinions effectively, leading to accurate outcomes based on evidence. Gathering Claims: The tool starts by gathering statements related to climate change from various sources.

Analyzing Claims: It breaks down these claims into smaller parts to understand them better. Climate+Tech launches an open source AI fact-checking system that combines multiple AI models to evaluate climate claims against scientific sources, achieving 85% agreement with expert consensus. The spread of climate misinformation poses a significant challenge in our digital age. Recent research highlights the scale of this problem: To address this challenge, we’re excited to announce the launch of Climate+Tech FactChecker, an open source AI-powered system designed to fact-check climate claims at scale while maintaining scientific rigor and transparency. Related Solution: This tool is part of our broader initiative on AI tools for society.

Learn more about our complete ecosystem of fact-checking and democratic discourse tools in “AI Tools for Society: Fact-Checking, Democracy & Climate Communication”. Our system’s unique approach mirrors how expert panels evaluate scientific claims. Multiple AI “advocates” examine evidence independently from different scientific sources, provide structured reasoning with citations, and a “mediator” AI reconciles their findings into a final verdict. We are excited to share a new open-access publication in Nature Reviews Methods Primers, co-authored by EClim members Christian Huggel and Veruska Muccione: 📰 “Automated fact-checking of climate claims with large language models”🔗 Read the paper The paper introduces CLIMINATOR, a novel AI-powered framework designed to enhance fact-checking of climate-related claims.

By integrating verified scientific sources into a structured debate format, this tool supports the fight against climate misinformation. This interdisciplinary effort brings together experts from climate science, finance, and artificial intelligence, showcasing how collaboration can drive innovation for climate action. This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed by authoritative sources like the IPCC reports and peer-reviewed scientific literature, Climinator employs an innovative Mediator-Advocate framework. This design allows Climinator to effectively synthesize varying scientific perspectives, leading to robust, evidence-based evaluations. Our model demonstrates remarkable accuracy when testing claims collected from Climate Feedback and Skeptical Science.

Notably, when integrating an advocate with a climate science denial perspective in our framework, Climinator’s iterative debate process reliably converges towards scientific consensus, underscoring its adeptness at reconciling diverse viewpoints into science-based, factual conclusions. While our research is subject to certain limitations and necessitates careful interpretation, our approach holds significant potential. We hope to stimulate further research and encourage exploring its applicability in other contexts, including political fact-checking and legal domains. In the ongoing debate on climate change, the truthfulness of public statements is regularly called into question, emphasizing the critical need for swift and reliable fact-checking. A case in point is the recent claim made by Sultan Al Jaber, the president of COP28 and chief executive of the United Arab Emirates’ state oil company Adnoc. On November 21, 2023, Al Jaber controversially asserted that “There is no science out there, or no scenario out there, that says that the phase-out of fossil fuel is what’s going to achieve 1.5C.”...

Recognizing this challenge, our paper introduces Climinator, a novel framework designed to assess climate-related claims, leveraging advancements in LLMs. Climinator -– an acronym for CLImate Mediator for INformed Analysis and Transparent Objective Reasoning -– not only evaluates the accuracy of statements but also enhances its verdicts with evidence-based reasoning and relevant literature references. In an era where information proliferates at an unprecedented pace, the task of manually reviewing claims for accuracy becomes increasingly resource-intensive and challenging. Over a decade ago, scholars warned that the exponential growth of online content would eventually overwhelm journalistic fact-checkers, diminishing news quality and contributing to societal harms like diminished government accountability (Cohen et al. 2011). This concern has given rise to a new strand of research in Natural Language Processing (NLP), namely automated fact-checking (Cohen et al.

2011; Vlachos and Riedel 2014a; Hassan et al. 2017; Graves 2018; Guo, Schlichtkrull, and Vlachos 2022). With misinformation spreading faster and deeper than factual news (Vosoughi, Roy, and Aral 2018), there is a pressing need for sophisticated tools capable of effective and real-time fact-checking. While early automated fact-checking tools, such as those based on the FEVER dataset (Thorne et al. 2018) and climate-focused datasets like climateFEVER (Diggelmann et al. 2020), have made significant progress, they often fall short in providing the nuanced reasoning necessary for a comprehensive understanding of complex claims.

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