Guarding the Gates of Knowledge

A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must. Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “ I Used AI to Fake a Scientific Paper ,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses. “He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his

AI-generated content threatens scientific integrity

AI-generated scientific papers can plausibly deceive current human and software review mechanisms, leading to potential erosion of trust and integrity in scientific publishing. This ongoing arms race between AI-generated misinformation and detection efforts threatens the reliability of peer-reviewed knowledge. However, current defenses still operate effectively, preventing widespread breakdown.

The article discusses the challenges of detecting AI-generated content in scholarly publishing. While it highlights the difficulty of trusting AI outputs, it also shows that current systems can keep pace with AI-generated slop. This suggests a balanced struggle rather than an overwhelming dystopian or utopian scenario.

SOURCE     IEEE Spectrum: AI (tier 1)
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DOCUMENT   e1f46865-55ef-4437-9b6f-b674ddb40869
           https://spectrum.ieee.org/ai-slop-trust-science
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EVIDENCE   2 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      -7  (Mixed or uncertain)
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CONFIDENCE 83%