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Interpretable vs. Explainable AI: What’s the Difference?
Interpretability and explainability are key to maximizing AI's full potential by providing more visibility into how AI work
4 Min Read
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As AI's influence grows, so does the need for transparency in its decision-making processes. How does it work under the hood? As everyone surges to adopt AI, it’s important to understand some of the mechanics. Interpretability and explainability are two key components of what you see at the end of an AI query response. In this post, we’ll walk through these concepts.
The fundamental distinction between interpretable and explainable AI lies in their approach to transparency:
Interpretable models are built to be understood from the ground up.
Explainable models provide retrospective clarification of their decision-making processes.
Interpretable AI
Interpretable AI models show their work, making it clear how they jump from input to output. This transparency is important for a few reasons:
It builds trust
It makes debugging and improvement easier
It reduces the risk of bias in outputs
Common types of interpretable AI include decision trees, rule-based models and linear regressions.
Real-world applications of interpretable AI include bank loan approval processes and fraud detection in credit card companies.
Explainable AI (XAI)
Explainable AI (XAI) acts as a translator for complex AI systems, breaking down their choices into human-friendly terms. This is crucial for:
Ensuring legal and ethical compliance
Building trust with users
Identifying and correcting biases
XAI employs techniques like feature importance analysis, LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive exPlanations).
Real-world examples of XAI include medical diagnosis assistance and decision explanation in self-driving cars.
Comparing Interpretability and Explainability in AI
Aspect | Interpretability | Explainability |
|---|---|---|
Model transparency | Provides insight into internal workings | Focuses on explaining specific decisions |
Level of detail | Granular understanding of components | High-level overview of complex processes |
Development approach | Designing inherently understandable models | Using techniques like SHAP or LIME |
Suitability for complex models | Less suitable due to transparency-complexity trade-off | Well-suited for explaining complex model decisions |
Challenges | May reduce performance for transparency | Can oversimplify complex processes |
Use cases | Credit scoring, healthcare diagnostics | Customer service automation, fraud detection |
A Use Case: AI Credit Scoring
Imagine a large bank, EagleBank, implementing an AI-powered credit scoring system to assess loan applications. This system analyzes various factors such as income, credit history, employment status and debt-to-income ratio to determine an applicant's creditworthiness.
EagleBank's AI model uses a combination of decision trees and linear regression, making it inherently interpretable. This allows loan officers to understand the key factors influencing the credit score:
Credit history contributes 35% to the final score
Current debt level accounts for 30%
Length of credit history impacts 15%
Recent credit inquiries affect 10%
Types of credit used influence the remaining 10%
This interpretability helps EagleBank ensure fairness in lending practices and comply with regulatory requirements.
While the model is interpretable, EagleBank also implements explainable AI techniques to provide clear justifications for individual decisions. When an application is rejected, the XAI system generates an explanation like this:
"Your loan application was declined primarily due to a high debt-to-income ratio (currently at 45%, while our threshold is 36%) and recent late payments on your credit card (3 in the last 6 months). Improving these factors could increase your chances of approval in the future."
By combining interpretability and explainability, EagleBank achieves several key benefits. It can demonstrate to regulators that its AI-driven decisions are fair and unbiased. Applicants receive clear, actionable feedback. The bank can also identify potential biases and errors in the model by analyzing explanations across multiple applications. Lastly, loan officers can better understand and explain decisions to customers, improving customer service.
Data Catalogs and AI Transparency
Data catalog platforms can support AI transparency by providing structured, accessible data management for AI models. They contribute to:
Enhanced data discovery and documentation
Improved data profiling and validation
Collaboration and communication among stakeholders
Data catalogs use knowledge graph architecture to improve AI model accuracy by 3x. They can:
Spot potential biases and inconsistencies in datasets
Streamline data profiling and validation processes
Provide a shared workspace for data scientists and business analysts
Improve metadata management
Modern data catalogs ensure that high-quality data is fed into training models without potential biases or inconsistencies that could skew results.
Finally…
Interpretability and explainability are key to maximizing AI's full potential by providing more visibility into how AI works. While data catalog platforms cannot directly explain how AI models work, they play a major role in paving the way for AI explainability.
As AI continues to evolve and integrate into various aspects of our lives, the importance of transparency in AI decision-making processes cannot be overstated. We can build more trustworthy, effective AI systems, but we need to understand where our AI answers are coming from.
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