Why can't AI explain its decisions?
Why can't AI explain its decisions?
Neural networks operate as black boxes with billions of parameters interacting in incomprehensible ways.
The opacity problem: If a LLM has 1.76 trillion parameters, no human can interpret how these weights combine to produce specific outputs. The European AI Act requires explainability for high-risk applications, but current LLMs cannot comply.
Research from MIT CSAIL shows:
- Mechanistic interpretability captures <1% of model behavior
- Attention visualizations misleading for understanding reasoning
- Post-hoc explanations often inaccurate rationalizations
Implications: Cannot debug errors systematically, impossible to guarantee behavior, liability unclear when harm occurs, audit requirements unmet.
Find more: AI Concerns Addressed.
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Why can't AI explain its decisions?
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