Those tools meant to help doctors 'double-check' AI are actually making them mess up even more. It’s like a GPS that makes you take a wrong turn.
March 27, 2026
Original Paper
Interpretability without Actionability: Mechanistic Intervention Methods Fail to Correct Clinical Triage Errors in Language Models
SSRN · 6437137
The Takeaway
Regulators assume that if we can see 'inside' an AI's head, we can correct its mistakes, but interventions based on these explanations often disrupt the model's correct behaviors while failing to fix the wrong ones. It suggests that AI 'transparency' might be an unhelpful or even dangerous tool for human oversight.
From the abstract
Background: Regulatory frameworks for clinical artificial intelligence presume that interpretability supports effective human oversight, but whether mechanistic interpretability methods can correct model errors has not been tested in a clinical domain.<br><br>Methods: We compared four mechanistic interpretability methods spanning the principal approaches to inference-time model intervention - inherent concept-level transparency, post-hoc dictionary learning, causal pathway tracing, and represent