Explainable AI (XAI): What It Is and How It Works

When a model flags a customer as high churn risk or denies a loan, the next question is predictable: Why? Explainable AI (XAI) gives data teams practical ways to show what drove a prediction, so stakeholders can validate results and act with confidence.

This article is part of Domo's learning content series, with hands-on guidance and practitioner stories for people building, deploying, and governing analytics and AI.

Key takeaways

Here are the main points to take away before getting into techniques and examples:

• Definition: Explainable AI is a set of techniques that make AI outputs understandable to the people who act on them, surfacing the reasoning behind predictions so stakeholders can trust and validate results.
• Architectural placement: XAI operates at the model output layer, sitting between raw mathematical predictions and final business decisions.
• Accuracy preservation: Explainability doesn't require sacrificing model performance. Many techniques work on top of high-performing complex models.
• Regulatory urgency: The General Data Protection Regulation (GDPR) Article 22 and the EU AI Act, fully applicable August 2, 2026, have pushed model transparency from research interest to operational requirement.
• Practical outcomes: Organizations that implement XAI see faster debugging, smoother compliance reviews, and higher adoption rates among business teams.

What is explainable AI

Explainable AI refers to methods that make the outputs and behavior of AI systems understandable to humans. The goal? Surfacing the factors, weights, or logic behind decisions so you can interpret, validate, and trust the results rather than accepting a prediction at face value.

Consider a machine learning model that flags a customer as high churn risk. Without explainability, the sales team sees a number. With it, they see that support ticket volume spiked while product usage dropped. That context changes whether anyone acts on the alert.

Three distinctions help clarify what XAI actually covers:

• Local vs global: Local explanations describe why a single prediction happened. Global explanations describe overall model behavior across all predictions.
• Intrinsic vs post-hoc: Intrinsic models are interpretable by design, like decision trees. Post-hoc methods explain black-box models after training.
• Model-agnostic vs model-specific: Model-agnostic techniques work on any algorithm. Model-specific techniques exploit a particular architecture's structure.

People often confuse explainability with interpretability. Interpretability usually refers to inherent model simplicity (think decision trees you can literally read). Explainability includes techniques applied to complex models. In practice, the terms overlap, and what matters is whether the explanation serves your audience's needs.

Why explainable AI matters

Someone will eventually ask why the system made a specific choice. That's not a theoretical concern. When a model influences credit decisions, hiring pipelines, or fraud alerts, the question comes fast. If the answer is "the team doesn't know," the model gets bypassed or the organization faces regulatory scrutiny.

The drive for transparency maps to concrete deliverables:

• Debugging and iteration: When predictions drift, explanations pinpoint which features changed. Without them, data teams waste cycles retraining entire models instead of fixing specific inputs.
• Regulatory compliance: GDPR Article 22 and the EU AI Act can require organizations to provide meaningful information about automated decisions that affect individuals, especially in high-risk or high-impact contexts. XAI produces the audit artifacts (model cards, feature logs) that satisfy reviewers.
• Stakeholder adoption: Business teams ignore models they don't understand. In a 2024 McKinsey survey, they found that 40 percent of respondents identified explainability as a key AI risk yet only 17 percent actively mitigate it. That gap explains why so many technically sound models never make it into daily workflows. Explanations convert skepticism into action.
• Bias detection: Explanations reveal when protected attributes or their proxies drive outcomes, enabling teams to intervene before deployment.

How explainable AI works

Teams with existing black-box models in production need post-hoc techniques. Teams building new models can choose interpretable architectures from the start. The right path depends on what's already deployed and how much accuracy tradeoff the business accepts.

Path one uses interpretable-by-design models. Linear regression, decision trees, and rule-based systems produce outputs humans can trace directly. These models may underperform on complex, high-dimensional data. Use this approach when regulatory requirements demand full transparency or when the accuracy gap is negligible.

Path two pairs black-box models with post-hoc explanation. Gradient boosting, neural networks, and ensemble methods often outperform interpretable models but hide their reasoning. Post-hoc techniques generate explanations after the fact. Use this when accuracy is critical and stakeholders accept approximated explanations.

Large language models (LLMs) present a distinct challenge. Their outputs are probabilistic text rather than structured predictions. Techniques like attention visualization and chain-of-thought prompting offer partial transparency, but the field is still maturing. Teams deploying generative models should expect to layer multiple explanation methods.

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Explainable AI techniques

The right method depends on whether the explanation is for a data scientist debugging a model, a compliance officer documenting decisions, or a person trying to understand a single prediction. No single technique serves all three audiences. Choosing the wrong one for a stakeholder can do more harm than having no explanation at all.

Feature importance and permutation

Feature importance ranks variables by their contribution to model predictions, typically measured by how much accuracy drops when a feature is shuffled.

A feature importance score looks authoritative. But if two features are highly correlated, the importance gets split arbitrarily between them. This leads teams to drop the wrong variable during feature selection. Validate by running drop-column importance: remove the feature entirely and retrain to confirm the ranking holds.

SHAP values

SHAP calculates each feature's contribution to a specific prediction by averaging its marginal effect across all possible feature combinations. The concept comes from cooperative game theory.

What makes SHAP different: The values are additive and sum exactly to the prediction difference from baseline. This mathematical property makes them audit-ready and comparable across observations.

In a waterfall plot, each bar shows how much a feature pushed the prediction above or below baseline. In a beeswarm plot, each dot represents one observation, with horizontal position showing the SHAP value and color showing the feature value.

A few things to watch. Baseline choice matters, and the default training mean may not match business intuition. SHAP distributes importance across correlated features, which can obscure the true driver. KernelSHAP runs slowly on large datasets; TreeSHAP is fast but only works on tree models.

LIME

LIME generates explanations by perturbing the input, observing how predictions change, and fitting a simple surrogate model to approximate local behavior.

It runs faster than SHAP. But here's where teams get burned: If two nearly identical inputs produce different explanations, stakeholders lose trust quickly. LIME is sensitive to kernel width settings, and stability varies. Always test on multiple similar inputs before presenting explanations to business teams.

Use LIME for rapid prototyping and exploratory analysis. For high-stakes decisions requiring reproducible explanations, SHAP's stability is worth the compute cost.

Counterfactual explanations

Counterfactual explanations identify the smallest change to an input that would produce a different prediction. They answer: What would need to be different for the model to say yes?

A loan applicant cares less about feature weights than about what steps would flip their outcome.

Not all counterfactuals are actionable. Telling someone to be five years younger isn't useful. Effective systems constrain changes to mutable, realistic features and minimize the number of changes required.

Explainable AI use cases

Generic statements about AI helping healthcare don't help teams evaluate fit. Specificity does.

A risk analyst reviews a loan denial. SHAP values show that high credit utilization and recent inquiries drove the score down. The analyst confirms the model isn't relying on zip code (a proxy for protected attributes) and approves the decision for compliance logging.

A customer success manager receives a high-churn alert. The explanation surfaces that support ticket volume spiked while product usage dropped. The manager prioritizes outreach with specific talking points rather than a generic check-in.

A fraud analyst investigates a flagged transaction. The model highlights unusual merchant category, high transaction velocity, and geographic distance from typical behavior.

A supply chain planner questions a spike in predicted demand. The explanation shows that a promotional calendar feature and regional weather data drove the forecast. The planner validates the inputs and adjusts inventory accordingly.

Explainable AI challenges and limitations

XAI techniques are approximations, not ground truth. Teams that treat explanations as definitive answers risk overconfidence.

Post-hoc explanations approximate model behavior. They don't replicate it exactly. Measure fidelity by comparing explanation-based predictions to actual model outputs on held-out data.

LIME and similar methods can produce different explanations for nearly identical inputs. Run stability tests across perturbed inputs before deploying explanations to users.

Feature importance splits credit among correlated variables unpredictably. Use SHAP with interaction values or apply feature grouping to handle correlated data.

SHAP on large datasets can be prohibitively slow. Use TreeSHAP for tree models or sample data for KernelSHAP.

Models can be designed to produce misleading explanations that hide true decision logic. Audit explanations against domain knowledge and alternative techniques.

GDPR Article 22 grants individuals the right to obtain meaningful information about the logic involved in automated decisions. This doesn't mandate a specific technique, but it requires understandable reasoning. The EU AI Act adds requirements for high-risk systems. Document your explanation methods and validate that outputs meet regulatory intent.

Best practices for explainable AI

Explanation quality degrades as the data drifts and models retrain. Teams that treat XAI as a one-time deliverable end up with stale explanations. You'll notice this pattern repeatedly: The initial implementation works, then six months later nobody trusts the outputs anymore.

During model development, choose explanation scope early. Decide whether stakeholders need local explanations, global explanations, or both. Document the baseline used for SHAP so future audits can reproduce results. Test for stability by running explanation methods on perturbed inputs before deployment.

At deployment, generate model cards documenting purpose, training data, performance metrics, and explanation methods. Define when explanations are required (all adverse decisions, all predictions above a confidence threshold).

Post-deployment, monitor whether feature importance rankings shift over time. This often indicates data drift.

Four criteria for evaluating XAI quality:

• Fidelity: Does the explanation accurately reflect model behavior?
• Stability: Do similar inputs produce similar explanations?
• Comprehensibility: Can the target audience understand without additional training?
• Actionability: Does the explanation support the decision the user needs to make?

How Domo supports explainable AI

Explanations are only useful if they reach the people who act on them. A SHAP value buried in a data science notebook doesn't help a sales manager understand a churn prediction.

Domo helps teams operationalize explainability by pairing governed data with orchestration, so explanations show up where decisions get made, with clear controls over who can see what and when.

At the foundation layer, Domo connects to source systems and prepares governed datasets that teams can use with their preferred models through Domo's AI Service layer. That governance carries through into how predictions and explanations are accessed and audited. Clean, consistent data improves explanation quality.

At the activation layer, Domo's AI agents and analytics tools surface driver analysis and contribution breakdowns inside the workflows where decisions happen, including dashboards, apps, and alerts. Business teams can see which factors influenced a prediction without switching tools. What-if scenarios help teams explore how changing inputs could shift outcomes.

Domo's agents operate with bounded autonomy: People set objectives, permissions, and approval steps, and the system executes within those constraints on governed data.

At the distribution layer, explanations travel with predictions into mobile apps, embedded analytics, and automated alerts. A field rep sees why a lead scored high. A finance analyst sees what drove a forecast variance. Role-based access controls ensure explanations reach authorized people in the right roles, and audit logs track who viewed which explanations and when.

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Final thoughts

Explainable AI is not a checkbox. It's an operational capability that matures alongside your AI investments. The techniques exist. The challenge is embedding explanations into workflows where decisions happen, maintaining quality as models evolve, and satisfying stakeholders from data scientists to regulators to business users.

Start with your highest-stakes models: those influencing customer outcomes, financial decisions, or compliance-sensitive processes. Choose techniques that match your audience (SHAP for audit-ready attribution, counterfactuals for user-facing recourse, feature importance for quick exploration). Document your approach, monitor for drift, and treat explanations as first-class artifacts alongside predictions.

Want to pressure-test your XAI approach with people who've been there, picking techniques, validating stability, and making explanations usable for the business? Join the Domo community and swap notes with practitioners building trustworthy AI.