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April 2025 peer-reviewed study shows even tiny prompt tweaks sway AI bias.
Tests show every prompt has built-in bias, worsened by order, labels, framing, and even asking “why.”
Newer models, GPT-4 included, output even stronger biases than GPT-3, and researchers conclude a truly neutral prompt and full objectivity is impossible. “there will never be such a thing as a neutral or perfect prompt,”
Prompt engineering cannot fix bias. Only mass-averaging prompt variations can, and is impractical for daily use. Meanwhile, doctors, lawyers, and editors may unknowingly anchor high-stakes decisions on these skewed outputs.
Beneath the surface, large language models crunch billions of numbers in tangled math no one can trace, so every answer is an educated guess, not a sure fact.
When doctors and lawyers depend on AI, will your fate rest on hidden AI bias?
Now journals vet papers with AI, will peer-review become contaminated?
Can science forge the intuition to craft AI beyond language models?
Prompt architecture induces methodological artifacts in large language models
submitted by /u/LukeNarwhal
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