An AI assistant for journalists and researchers that automatically fact-checks claims within an article. It cross-references statements against a database of trusted sources and flags claims that are unsubstantiated, misleading, or false.
Newsrooms, publishers and content teams face tighter deadlines and fewer staff while the cost of errors keeps rising. A misread statistic, a misattributed quote or an outdated figure can lead to corrections, complaints and loss of trust. Dedicated fact-checkers are a luxury many small and regional outlets cannot afford, so verification is left to writers and editors who are already stretched and may not notice when a claim has quietly drifted from its source.
An AI fact-checking tool supports that work. It scans a draft, pulls out checkable claims such as numbers, dates, quotes and attributions, searches trusted sources and the newsroom's own archive, and shows the editor what supports or contradicts each claim. It does not rule on truth. Its value is catching things worth a second look and putting the relevant source in front of a human quickly, so editors spend time judging evidence rather than hunting for it.
The writer or editor submits a draft from the CMS or a document editor, and the tool splits the text into individual claims with their surrounding context.
Each claim is classified by type, such as statistic, quote, date or attribution, and matched against a curated list of trusted sources and the publication's archive.
Semantic search and retrieval find relevant passages from official statistics, published research, court records, fact-check databases and prior reporting.
An LLM compares the claim with the retrieved evidence and labels it as supported, contradicted, unclear or unverified, quoting the evidence and linking to it.
The editor reviews flagged claims, contacts sources where needed and records decisions, building an audit trail for the article.
Before publishing an article, a newsroom runs it through an AI fact-checker. The AI flags a statistical claim and provides a link to the original research paper, which shows the journalist misinterpreted the data, preventing the publication of misinformation.
NLP, Fact-checking Databases (e.g., Snopes API), RAG, Semantic Search
Defamation law across Australian states and territories still applies to published content, and an AI label of 'supported' is no defence if the underlying claim is wrong, so legal review of risky stories remains essential. Publishers that are members of the Australian Press Council, or bound by broadcasting codes, must meet accuracy and correction standards regardless of the tools they use. Drafts may contain confidential source information, so check where text is processed and whether it is retained by the provider. Reusing extracts from third-party reports for checking is generally fine internally, but republishing them requires attention to the Copyright Act 1968.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
No. It can find and compare evidence, but truth often depends on context, definitions and sources the tool cannot access, such as interviews. The label is a triage signal; the editor still makes the call and is responsible for what is published. Newsrooms should say so plainly in their AI policy.
Start with primary sources relevant to your beat, such as Australian Bureau of Statistics releases, Hansard, court judgments, company filings and peer-reviewed research, plus your own archive. Add fact-check databases where licensing allows. Avoid open web search as the only source, since it surfaces low-quality material easily.
Two to four weeks is typical for a working pilot. That covers CMS integration, building the source list, writing claim extraction and comparison prompts, and testing on published articles where the correct answers are already known. Editorial training and policy updates should happen during the pilot.
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