What is Explainable AI (XAI)?

Introduction

As the world evolves, so do the tools businesses and services need to adapt and grow to deliver stronger customer experiences. One of the most prominent topics across all sectors today is Artificial Intelligence, or AI, as it will be referred to throughout this article. AI enables faster processing, reduces workloads, and provides more accurate predictions. While the capabilities and benefits of AI are still debated, businesses and their employees need to understand what happens “inside the black box” and what outcomes it produces.

What is Explainable AI (XAI)?

The concept of Explainable AI (XAI) refers to a set of methods designed to help users understand AI models, their outputs, and how inputs are used, while also providing transparency to the business. The term black box is often used to describe a machine or model that is fed data and produces an output without clear insight into how that output was generated. With XAI, businesses gain visibility into how their models or functions operate—not just how data goes in and results come out.

Why is XAI important?

The primary reason is trust and transparency, which are frequently cited concerns. Businesses and employees should be able to trust and understand the predictions or output created by AI models to feel confident using them. Another critical reason for understanding how AI models work is the ability to identify and reduce biases or errors within the algorithms. Because AI systems are trained using human-generated data, biases can be unintentionally introduced. In some cases, organizations may view data as “correct” simply because it aligns with a desired outcome or narrative, rather than because it is truly representative or accurate.

To prevent inaccurate results, businesses and employees should continuously analyze and monitor model outcomes. This can be achieved through careful evaluation of training data. By feeding well-curated and representative training data into AI models, organizations can produce more accurate and reliable predictions when processing real-world production data. Compliance is another key reason for understanding AI tools, as blindly trusting model outputs can create regulatory risks and harm customer trust or the organization’s reputation.

How does XAI work?

Within XAI, there are three widely used techniques for interpreting models and understanding how features influence outcomes: LIME, SHAP, and PFI.

LIME, or Local Interpretable Model-Agnostic Explanations, is designed to improve the explainability and transparency of AI models by making small changes to input features and observing how those changes affect predictions. These perturbed inputs and corresponding outputs are used to create a simplified dataset, which can then train a local replacement model. This replacement model focuses on the most influential features near the original input, helping businesses understand which data points most strongly impacted a specific prediction. LIME captures the local decision-making behavior of complex models in an interpretable way.

The next technique, SHAP (Shapley Additive exPlanations), is more technically advanced and provides deeper insight into feature contributions. SHAP is based on game theory and treats features as “players” cooperating to produce a prediction. It calculates how much each feature contributes to the difference between a baseline prediction and the final output by averaging its marginal contribution across all possible feature combinations. This approach helps explain how a model arrives at a specific decision, builds trust, and supports bias detection.

The final technique, Permutation Feature Importance (PFI), measures feature importance by randomly shuffling the values of a feature and observing the impact on model performance. If performance degrades significantly after shuffling, the feature is considered important; if performance remains largely unchanged, the feature is deemed less important.

A financial services example illustrates this concept well. If applicants’ credit scores are randomly mixed up, high-risk applicants may appear low-risk, and low-risk applicants may appear high-risk. As a result, the model may approve riskier loans, leading to higher default rates, increased regulatory risk, and lost revenue. This performance drop clearly demonstrates that credit scores are a highly important feature.

Conclusion

Explainable AI is a rapidly evolving area, with new models and tools emerging regularly. However, the overarching goal for businesses adopting AI remains consistent: to promote trust, transparency, and accountability among employees, shareholders, and customers.