Interpretability Needs Clarity
What We Mean When We Say We Want to Understand AI
Everyone agrees AI systems should be interpretable. Nobody agrees what interpretability means. This confusion leads to requirements that sound good but don’t specify what’s actually needed.
Clarifying what we want from interpretability makes it achievable.
Different Meanings of Interpretability
When people ask for interpretable AI, they might want:
Global understanding: How does the model work overall? What features matter? What patterns did it learn?
Local explanations: Why did the model make this particular decision? What inputs drove this output?
Counterfactual reasoning: What would have changed the decision? How close was it to going the other way?
Debugging information: When the model fails, what went wrong? Where in the process did the error occur?
Trust calibration: How confident should I be in this output? When should I override the model?
Auditability: Can we demonstrate the model meets requirements? Can we reproduce decisions?
These are different needs requiring different approaches. “Make it interpretable” without specifying which type leads to solutions that miss the mark.
Audience Matters
Who needs to interpret the system shapes what interpretability means:
End users need actionable understanding. Not how the model works, but whether to trust this output and what to do with it.
Operators need debugging capability. When something goes wrong, can they diagnose and intervene?
Regulators need compliance evidence. Can decisions be explained? Can bias be assessed?
Developers need training insight. Where is the model wrong? How can it be improved?
The same system might need different interpretability for different audiences.
The Trade-off Space
Interpretability often trades off against other goals:
Accuracy vs. interpretability. Simpler models are more interpretable but sometimes less accurate. When accuracy matters most, this trade-off is real.
Speed vs. interpretability. Generating explanations costs compute. Real-time systems may not afford it.
Security vs. interpretability. Detailed model explanations can enable adversarial attacks. Security-sensitive applications may limit what’s revealed.
These trade-offs don’t resolve by declaring interpretability important. They require explicit decisions about what matters most for each use case.
Making Progress
Practical steps toward useful interpretability:
Specify the need. What kind of understanding? For whom? For what purpose? Vague requirements produce vague solutions.
Match method to need. Feature importance, SHAP values, attention visualization, counterfactual examples - each technique serves different needs. Choose based on specified requirements.
Test understanding. Can the target audience actually use the interpretability provided? Does it change their decisions appropriately? Test with real users.
Accept appropriate limits. Full transparency into complex model internals may not be achievable. Useful interpretability for specific purposes usually is.
Iterate. First attempts at interpretability often miss the mark. Build in feedback loops to improve.
The Honest Conversation
Some systems resist interpretation. Large neural networks make decisions through patterns that don’t reduce to human-comprehensible rules.
This creates an honest question: Is interpretable AI required for this use case, or is interpretable behavior sufficient?
Sometimes we need to understand why. For high-stakes decisions affecting individuals, explanation matters for fairness and appeal.
Sometimes we need predictable behavior. The system reliably does what it should, even if we can’t explain the internal mechanism.
Both are legitimate. Conflating them creates confusion about what’s needed and achievable.
What kind of interpretability do your AI systems actually need? Who needs to understand what, and for what purpose?
Related: 05-molecule—multi-dimensional-llm-evaluation-framework, 05-atom—uniform-confidence-problem