What is Explainable AI?

Explainable AI (XAI) is a set of methods and practices designed to make the decisions and outputs of artificial intelligence systems understandable to humans. Unlike “black box” models, where the reasoning is opaque, explainable AI techniques provide visibility into why a machine learning model made a specific prediction, recommended an action, or generated a piece of content. Implementing explainable AI is necessary for businesses to create stakeholder trust, debug model performance, meet regulatory requirements, and align them with Responsible AI for transparent and fair automated decisions.

Explainable vs. Interpretable AI

Interpretable AI usually refers to models that are transparent by design (e.g., simple decision trees or linear models. See Interpretable forecasting models), so humans can follow their internal logic directly. Explainable AI, by contrast, adds explanation layers around more complex or opaque models, using techniques such as feature attribution or local approximations. In enterprise settings, both approaches support AI transparency and explainability for governance, risk, and compliance.

Common explainable AI methods and techniques

Explainable AI uses multiple approaches to expose how models arrive at decisions. Different methods work best for different types of models and use cases.

• Feature importance methods: Techniques like LIME explainable AI (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) assign a score to each input feature, showing exactly how much each variable contributed to a specific prediction.​​
• Visualization-based explanations: Visual techniques like heatmaps and saliency maps highlight which parts of an image or text a model focused on when making a decision, commonly used in computer vision and NLP.
• Surrogate or simplified models: This explainable AI technique trains a simpler, interpretable model (like a decision tree) to mimic the behavior of a complex “black box” model. They provide a readable structure that helps understand the core decision paths without exposing sensitive logic or source code.
• Counterfactual explanations: These methods answer “what if” questions by identifying the minimal change needed in an input to alter the model’s output (e.g., “If income were 10% higher, the loan would have been auto-approved”).​
• Explainability for deep learning: Deep neural networks require specialized techniques as their internal layers are harder to interpret than simpler models. Methods like attention mechanisms show which parts of sequential data the model prioritizes, while layer-wise relevance propagation traces how specific inputs influence outputs. It is feasible to audit models used in vision, natural language processing, supply chain, and forecasting applications. MLOps platforms embed these explainability checks into model pipelines, ensuring explanations remain accurate as models retrain and drift.

Explainable AI frameworks, governance and compliance

Explainability is embedded into how businesses govern AI systems and ensure compliance with changing regulations. Key compliance drivers include GDPR’s “right to explanation,” which gives users the ability to request clear reasoning behind automated decisions that affect them. 

The EU AI Act establishes transparency obligations for high-risk AI deployments, requiring organizations to document and explain AI system behavior before and after deployment. 

Explainability also supports responsible AI programs by making model logic auditable, enabling bias detection, and maintaining stakeholder trust in high-stakes domains like finance, healthcare, and employment decisions. 

Explainable AI solutions in practice

Explainable AI moves from theory to practice when it is tied to specific decisions, users, and risks. Although it is applied differently across different industries, the goals stay the same: make model reasoning visible, controllable, and auditable.​

• Finance: Explainable AI in finance supports regulatory compliance, credit scoring, fraud detection, and portfolio risk models by showing which factors drove each prediction and how sensitive the outcome is to changes in those factors. Risk and compliance teams use these explanations to justify approvals or rejections, align models with policy, and answer regulator questions about automated decision-making.​
• Healthcare: XAI helps clinicians understand why a diagnostic model flagged a patient as high risk or recommended a specific treatment path. Feature attributions, saliency maps, and counterfactual examples let medical experts compare model reasoning with clinical guidelines before trusting AI in frontline workflows.​
• Retail and e-commerce: Explainable AI applications include transparent personalization, pricing optimization, fraud detection, and provide next best actions. XAI in retail surfaces which behaviors, product attributes, or session signals influenced recommendations or risk scores, which aligns with how explainable AI is described as building trust in automated systems.
• Customer analytics: Explainable AI use cases include churn modeling, trade promotion recommendations, and segmentation. When predicting customer churn, feature importance methods reveal which signals (declining usage, tenure, unresolved support ticket, or contract terms) matter most to the model’s churn score. This transparency allows retention and churn prevention teams to intervene with precision rather than broad campaigns. Tools like Vertex Explainable AI surface signals directly in the ML workflow, so insights flow from model to action without manual analysis overhead.​
• Supply chain and operations: Explainable AI models help with demand sensing and forecasting, IoT monitoring and analysis, and warehouse optimization by highlighting the drivers behind each prediction or recommended action. For example, in reducing warehouse order-picking time, XAI-style diagnostics help planners see when forecasts are driven by seasonality, promotions, or anomalies, making it easier to challenge, override, or adjust model outputs before they impact production or fulfillment.