FDR_Reagan
Platinum Member
- Nov 20, 2023
- 3,714
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Q: Did you find grok more reliable than google ai?
IMHO, only some of the times.
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Summary of “AI and Fact-Checking: When Probability Replaces Evidence”
Tal Hagin, November 13, 2025 (Fathom Journal)
In today’s fractured, high-speed media environment, trust in legacy outlets and traditional fact-checking has collapsed, creating information vacuums that bad actors exploit (e.g., false claims after the 2024 Bondi and Portland stabbings). Into this gap have stepped Large Language Models (LLMs) such as ChatGPT, Grok, Gemini, and Perplexity, which are now widely used as de-facto fact-checkers because they deliver instant, confident, polished answers that feel neutral and authoritative.
Core argument:.
LLMs do not verify facts; they predict statistically probable text.
Concrete failures cited:
1. Grok repeatedly misidentified recent Gaza photos as old images from Yazidi/Iraq or ISIS/Mosul contexts because of superficial visual and thematic similarities, ignoring geolocation and primary sources.
2. Similar misattribution of a booby-trapped doll photo (actually from Yemen 2018) to 2016 Mosul.
3. In low-coverage conflicts (e.g., Sudan), scarce reliable reporting means training and retrieval data are dominated by repeated narratives or even AI-generated fakes, embedding structural bias.
Sources of inherent bias:
Conclusion:
LLMs are powerful research assistants (useful for narrowing searches, brainstorming, pattern spotting) but dangerous replacements for fact-checkers. They trade evidence for probability, speed, and apparent neutrality. Human rigor—primary-source verification, geolocation, linguistic competence, and critical oversight—remains indispensable. In an era where perception routinely outruns truth, treating AI outputs as authoritative risks turning statistical likelihood into accepted fact, accelerating rather than curbing misinformation.
Key takeaway: Use LLMs to augment, never to replace, evidence-based human judgment.
IMHO, only some of the times.
^^^^^^^^^
Summary of “AI and Fact-Checking: When Probability Replaces Evidence”
Tal Hagin, November 13, 2025 (Fathom Journal)
In today’s fractured, high-speed media environment, trust in legacy outlets and traditional fact-checking has collapsed, creating information vacuums that bad actors exploit (e.g., false claims after the 2024 Bondi and Portland stabbings). Into this gap have stepped Large Language Models (LLMs) such as ChatGPT, Grok, Gemini, and Perplexity, which are now widely used as de-facto fact-checkers because they deliver instant, confident, polished answers that feel neutral and authoritative.
Core argument:.
LLMs do not verify facts; they predict statistically probable text.
- Pure generative models (e.g., base ChatGPT) have no real-time knowledge and simply reproduce patterns from training data.
- Retrieval-augmented models (Grok, Perplexity, Gemini) fetch current web sources but still do not independently verify them; they assume retrieved material is reliable and match inputs to the most statistically likely context.
Concrete failures cited:
1. Grok repeatedly misidentified recent Gaza photos as old images from Yazidi/Iraq or ISIS/Mosul contexts because of superficial visual and thematic similarities, ignoring geolocation and primary sources.
2. Similar misattribution of a booby-trapped doll photo (actually from Yemen 2018) to 2016 Mosul.
3. In low-coverage conflicts (e.g., Sudan), scarce reliable reporting means training and retrieval data are dominated by repeated narratives or even AI-generated fakes, embedding structural bias.
Sources of inherent bias:
- Training data reflect what is most visible online; repressed or low-coverage regions (most of the Middle East, per Freedom House) produce skewed, incomplete datasets.
- Fine-tuning and source-selection criteria further amplify dominant narratives while marginalised perspectives vanish.
Conclusion:
LLMs are powerful research assistants (useful for narrowing searches, brainstorming, pattern spotting) but dangerous replacements for fact-checkers. They trade evidence for probability, speed, and apparent neutrality. Human rigor—primary-source verification, geolocation, linguistic competence, and critical oversight—remains indispensable. In an era where perception routinely outruns truth, treating AI outputs as authoritative risks turning statistical likelihood into accepted fact, accelerating rather than curbing misinformation.
Key takeaway: Use LLMs to augment, never to replace, evidence-based human judgment.
