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
TLDR:
-Doctor prompts your symptoms.
-Unless LLM running factorial variation of said prompt to massive degree = bias in response.
Example:
Doctor A
“Patient has:
-Runny nose
-Cough
-Fever”
Doctor B
“Patient has:
-Cough
-Runny nose
-Fever”
LLM biases more to Runny nose having importance for Doctor A. Cough for Doctor B. Even if Doctor did not clarify that.
This is one of innumerous biases. Study covers multiple and suggests it is impossible to fix this in one prompt. Furthermore, evidence that attempts to prompt engineer/outsmart LLM may paradoxically cause more biased responses.
Responding to non-thinkers in advance:
“But they’ll use RAG”
Besides the point. This is about training data, inherent to LLMs.
“Surely the medical LLMs know about this and would be employing the factorial methods in said peer-reviewed study.”
Nope. Hitherto not covered in media at all.
“Not true.”
Wrong, read the peer-reviewed study. Your hot take 3-second impulse anecdote is irrelevant.
This is a training data inference issue. Not improving in new LLMs. Lives at stake. Worth the 5 minute read while you take your shit.
Prompt architecture induces methodological artifacts in large language models
[link] [comments]