Interpretability and explainability are terms often used when discussing how well humans can understand artificial intelligence systems. Interpretability refers to the degree to which a human can predict the system’s output given a set of inputs.
Explainability refers to the ability to describe the reasoning or cause behind a system’s decisions or behavior.
This topic matters because regulators, users, and internal risk teams need to know how decisions are made by AI. Without a clear view into how or why a system produces an output, it’s harder to trust, audit, or correct it. Interpretability and explainability affect fairness, legal accountability, and user trust.
“Around 62% of data scientists say they cannot fully explain the decisions of their models to non-technical stakeholders.”
(Source: MIT Sloan Management Review, AI in the Enterprise 2023)
Why interpretability and explainability are often confused
These two terms are often mixed up, but they serve different goals. Interpretability is about how simple and understandable a model is by default. If you can look at the system and know what it’s doing, it’s interpretable.
Explainability comes into play when a system is too complex to be directly understood. In these cases, post-hoc tools or summaries help make sense of what the system has done. Deep learning models are typically not interpretable but can be made explainable through various methods.
Practical examples in the real world
Imagine a hospital using a machine learning model to recommend treatment plans. If the model is a decision tree, doctors can read its structure and understand why a choice was made. That model is interpretable.
Now consider a neural network trained on thousands of medical images. A radiologist may not understand what the model “sees.” Here, explainability tools like LIME or SHAP can show which pixels influenced the decision, giving users confidence even when they cannot directly interpret the system.
Legal systems may demand one or the other. The EU AI Act and frameworks like ISO/IEC 42001 call for appropriate transparency. In safety-critical sectors, choosing between interpretability and explainability has direct compliance implications.
Best practices for selecting and applying both concepts
The right approach depends on context, risk level, and audience. Assume not all users of an AI system will be technical experts, so simplicity and clarity matter.
Recommended best practices:
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Choose simpler models when possible: If accuracy is not sacrificed, use models like decision trees or linear regression for easier interpretability.
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Use explanation tools for complex systems: Techniques like SHAP, LIME, or counterfactual explanations help explain black-box models.
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Document the explanation method: Clearly state how the model’s behavior is being interpreted or explained and who it is designed for.
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Match explanation to the user: Technical teams may prefer statistical confidence intervals, while customers may want plain-language summaries.
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Evaluate explanation quality: Use user testing to check if people understand and trust the explanation method.
FAQ
Why can’t all AI systems be interpretable?
Some AI systems solve problems that require many layers of data processing or abstraction. This makes them powerful but hard to directly interpret. In such cases, explainability tools are used to make sense of the outputs.
Is explainability always needed?
Not always. For low-risk applications or when the outcome is trivial, explainability might not be necessary. However, when decisions impact safety, rights, or finances, explainability is critical for accountability.
Can I use interpretability and explainability together?
Yes. You can design a system with interpretable parts and use explainability tools to help users understand the more complex elements. This layered approach helps balance performance and trust.
What tools can help with explainability?
Common tools include SHAP, LIME, and What-If Tool. These tools work with a variety of models to offer local and global explanations.
Are there regulations requiring explainability?
Yes. The EU AI Act and other laws like GDPR require meaningful information about automated decision-making. This includes how the decision was made and the possible consequences.
Summary
Interpretability and explainability are both essential for responsible AI. While interpretability focuses on model simplicity, explainability offers insights into complex systems. Choosing the right strategy depends on who needs to understand the system and why. Clear documentation, user testing, and alignment with legal frameworks are essential parts of success.
