AI: Why fact-checking matters (with tips)
AI can be powerful but also prone to inaccuracies. Fact-checking is key to ensuring the reliability of AI-generated information and understanding its limitations is crucial.
Artificial intelligence (AI) has revolutionised the way we access and process information. However, AI systems are not perfect and can sometimes "make stuff up" or provide inaccurate information.
This phenomenon is known as "hallucination" in the field of AI. To mitigate this issue, developers are using various techniques to ground AI models in factual information, reports the University of Arizona.
One way to reduce AI hallucinations is to combine AI models with web search results. This allows the AI to search the web, read relevant pages and summarise the information with links to the sources.
While this approach is not foolproof, it significantly reduces the likelihood of inaccuracies. Many major AI models, including Perplexity AI, Microsoft Copilot, Google Gemini, Claude and ChatGPT, now incorporate web search capabilities.
Another approach is to use scholarly sources as a foundation for AI models. Systems like Elicit and Consensus use peer-reviewed research papers to provide accurate information. Elicit is a research assistant that automates parts of researchers' workflows, including literature reviews. Consensus is a search engine that surfaces claims made in peer-reviewed research papers, providing word-for-word quotes from relevant papers.
Fact-checking is crucial when working with AI models, especially when it comes to critical decision-making or research. While AI can provide valuable insights and information, it's essential to verify the accuracy of the information through multiple sources. By doing so, we can ensure that AI systems provide reliable and trustworthy information.
Fact-checking is a vital step in ensuring the accuracy and reliability of AI-generated information.
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