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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Use an AI detector to look for signals that text or media may have been generated or manipulated; use a fact-checking website to investigate whether a specific claim is true, false, or misleading. Neither is a final verdict. For an important decision, trace the claim or media to its original evidence and corroborate it with independent sources.
What each method can—and cannot—tell you
| Method | Question it addresses | Best use | Key limitation |
|---|---|---|---|
| AI-generated-content detector | Does this text or media show signals associated with AI generation or manipulation? | A preliminary clue when authorship or possible manipulation matters. | A score does not establish whether a claim is true. Results depend on the detector and test conditions. NIST’s evaluation and a USENIX study describe important limits. |
| Fact-checking website | Is this particular checkable claim accurate, false, or misleading in context? | Understanding an investigation, its evidence, and the reasoning behind its conclusion. | Coverage is selective; a new, local, or niche claim may not have been checked. Full Fact’s description of its work and a Reuters Institute review emphasize editorial judgment and context. |
| Reader-led verification | Can I trace this claim or image to reliable original evidence and corroborate it? | Checking origin, date, location, primary records, and independent reporting. | It takes time, and the available evidence may remain incomplete. AP’s verification guidance and Full Fact’s examples offer practical clues. |
Authorship and truth are separate questions: a true statement can be AI-written, while false information can be written by a person. A genuine photo can also be paired with a false caption. A detector cannot, by itself, prove a claim false, identify who created an image, or establish when and where a video was recorded.
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How reliable are AI detectors?
Performance depends on the task
NIST’s 2024 Generative AI pilot assessed text-to-text generation and discrimination using groups of articles and human- and machine-generated summaries. In that defined benchmark, detectors remained reasonably effective, but performance varied substantially: some generators deceived most discriminators, while some discriminators detected almost all tested generators. NIST said, “There is certainly room for improvement for both generator and discriminator systems.” The result is not a guarantee for every language, model, text length, editing process, or live news setting.
Real-world misinformation is harder to classify
A 2025 USENIX Security Symposium synthesis and replication work found that many misinformation-detection tasks differ from the challenges faced by real services. The authors highlighted non-representative datasets and evaluations that may not be independent of model training, and concluded that fully automated systems have limited efficacy in detecting human-generated misinformation. That makes detector output a lead to investigate, not a factual ruling.
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What a fact-checking website adds
Fact-checkers monitor news, politics, and social media for checkable claims. Tools can help with monitoring, but choosing a claim to investigate and assessing its context still involve editorial work. The Reuters Institute review says, “Much of the terrain covered by human fact-checkers requires a kind of judgement and sensitivity to context that remains far out of reach for fully automated verification.” It also concludes that automated systems will require human supervision for the foreseeable future.
A useful fact-check identifies the exact claim, explains the evidence and context, links to primary material where possible, and makes clear what remains uncertain. A rating without an inspectable explanation is less informative. Coverage is selective: no existing fact-check does not mean a claim is true or false. For breaking news, local events, or newly circulating media, consult primary local sources and wait for corroboration when possible. The available sources do not establish a universal coverage rate for fact-checking websites.
A practical way to check a suspicious story, claim, or image
- Write down the exact claim. Separate a factual statement from opinion, commentary, and a caption. Decide whether you are asking whether something was AI-made or whether its claim is true.
- Search for an existing fact-check. Search the wording, image, or central claim on established fact-checking sites. Read the explanation and follow its linked sources rather than relying on the rating alone.
- Trace the media to an earlier source. Use reverse-image search for a photo. For video, AP suggests taking a screenshot and searching for it. Look for an earlier appearance, the original account, and the upload date; an authentic old image can be misleading in a new context.
- Check primary and independent sources. Look for records, statements, complete footage, or reporting that addresses the precise time and place. Consult multiple verified sources, as AP recommends, and distinguish independent corroboration from outlets repeating one original report.
- Treat detector and provenance results as clues. Check which media and models a tool supports and whether it explains its result. A positive or negative score remains uncertain. A watermark may help identify a source, but its absence does not prove authenticity: watermarks can be missing or removed.
- Pause before sharing. If the evidence is incomplete, describe the uncertainty or wait for better sourcing instead of presenting the claim as established fact.
What one recent UK sample says about potential harm
Full Fact analyzed 112 selected fact checks and articles about AI-generated or AI-altered material seen in the UK between 1 January 2025 and 31 March 2026. The report says the sample is not exhaustive. Within that sample, 94 entries (83.9%) were assessed as creating a substantively false or misleading understanding, while 18 (16.1%) were assessed as only narrowly inaccurate. Full Fact assessed 46 entries (41.1%) as having substantive potential to cause or contribute to one or more specific consequences; 66 (58.9%) had no or very limited potential for such consequences.
These figures describe Full Fact’s selected sample and harm-risk assessment, not the prevalence of AI misinformation online or the share of all AI misinformation that is harmful. They illustrate why falsity and consequence are distinct questions.
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