What is Explainable AI?

What is Explainable AI?

Explainable AI, often abbreviated as XAI, refers to artificial intelligence systems and techniques that produce results which humans can understand and interpret. In contrast to "black box" AI models that provide outputs without revealing their reasoning, explainable AI makes it possible to understand why a model made a particular prediction, recommendation, or decision.

For business leaders, explainability is not about understanding the mathematics behind every algorithm. It is about being able to answer a straightforward question: "Why did the AI system make this recommendation or decision?"

Why Explainability Matters

Regulatory Requirements

Regulators increasingly expect organisations to explain AI-driven decisions, especially those that affect individuals. The European Union's AI Act includes explainability requirements for high-risk AI systems, and this trend is reaching Southeast Asia. Singapore's Model AI Governance Framework emphasises the importance of explainability, and Thailand's AI Ethics Guidelines include transparency as a core principle.

Customer Trust

When customers understand why they received a particular recommendation, approval, or denial, they are more likely to trust the system and the organisation behind it. Conversely, opaque AI decisions breed suspicion and complaints.

Internal Adoption

Employees and managers are more likely to use and trust AI tools when they can understand the reasoning behind outputs. A sales team, for example, is more likely to act on AI-generated lead scores if they can see the factors that contributed to each score.

Debugging and Improvement

When you can explain what an AI model is doing, you can identify when it is doing something wrong. Explainability is essential for detecting bias, finding errors, and improving model performance over time.

Levels of Explainability

Not all AI applications require the same level of explainability. Consider these levels:

Global Explainability

Understanding the overall behaviour of a model: what features are most important, what general patterns it has learned, and how different inputs affect outputs across the entire dataset. Useful for model validation and governance.

Local Explainability

Understanding why a model made a specific decision for a specific input. For example, why was this particular loan application denied? What factors contributed most to that decision? This is what individual customers and frontline staff typically need.

Model-Specific vs. Model-Agnostic Methods

Some explainability techniques are designed for specific model types, while others can be applied to any model. Model-agnostic methods like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are widely used because they work regardless of the underlying model architecture.

Explainability Techniques in Practice

Several practical techniques make AI systems more explainable:

• Feature importance: Ranking the input variables that most influence a model's output. For example, showing that a credit scoring model weights payment history and debt-to-income ratio most heavily.
• SHAP values: Quantifying the contribution of each feature to a specific prediction. This allows you to explain individual decisions with precision.
• LIME: Creating simplified, interpretable models that approximate the behaviour of a complex model around a specific prediction point.
• Decision trees and rules: Using inherently interpretable model architectures where the decision logic can be directly read and understood.
• Counterfactual explanations: Explaining decisions by showing what would need to change to get a different outcome. For example, "Your loan application was denied. If your annual income were 15% higher, it would have been approved."

Explainable AI in Southeast Asia

Explainability is becoming a key concern across ASEAN markets. Singapore's IMDA has included explainability as a principle in its Model AI Governance Framework, and the AI Verify toolkit includes modules for testing and documenting model explainability. Financial regulators in the region, including the Monetary Authority of Singapore (MAS) and Bank Indonesia, are increasingly focused on the explainability of AI systems used in financial services.

For businesses serving customers across multiple ASEAN markets, explainability takes on additional dimensions. Explanations may need to be provided in multiple languages and adapted to different levels of digital literacy. A clear, jargon-free explanation that works for a customer in Singapore may need to be adapted for customers in rural Indonesia or Thailand.

Implementing Explainability

1. Determine your explainability needs: Not every AI system needs the same level of explainability. High-risk decisions affecting individuals require more detailed explanations than internal operational optimisations.
2. Choose appropriate techniques: Select explainability methods that match your model type, audience, and regulatory requirements.
3. Design explanations for your audience: Technical stakeholders need different explanations than customers or regulators. Create multiple explanation formats as needed.
4. Document your approach: Record your explainability methods, their limitations, and how explanations are communicated. This supports governance and regulatory compliance.
5. Test understanding: Validate that your explanations are actually understood by their intended audiences through user testing and feedback.