XAI (Explainable AI): Definition and Use in Pricing
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Do your teams know why a price is recommended to them?
Let's plan an exchangeDiscover our pricing optimization softwareExplainable AI, or XAI (explainable AI), refers to the set of methods that make a model’s decision transparent: which variables were considered, in what way, and with what weight. In pricing, it answers the question that a buyer asks about every recommendation: Why this price, and not another? Without this answer, a fair price often goes unused.
The Essentials in 6 Questions
Methods that explain the output of an AI model.
Pricing managers, category managers, sales management, and internal audit.
When approving a recommendation, and then during audits.
In the pricing tool, next to each recommended price.
Get the recommendations accepted and be able to defend them.
Interpretable models, variable contributions, counterfactuals.
How does explainable AI work?
There are two approaches: choosing a model that is inherently easy to understand, or explaining a more complex model after the fact.
| Family of methods | Principle | What it shows |
|---|---|---|
| Interpretable model | Regression, short decision tree | A calculation rule that is easy to read as is |
| Significance of Variables | Measuring the weight of each variable on the set of predictions | What generally matters for the model |
| SHAP | Shapley values, derived from game theory | The contribution of each variable to an accurate prediction |
| LIME | A simple model fitted locally to a specific case | A clear approximation of a given decision |
| Counterfactual | The smallest change that would alter the result | "The price would have been different if…" |
We distinguish between global explanations, which describe a model's behavior across an entire catalog, and local explanations, which justify a specific recommendation.
In pricing, it's the second one that matters on a day-to-day basis. It applies to machine learning models that estimate demand and suggest prices.
Why Explainable AI Matters in Pricing
A recommended price is only meaningful if it is followed. And a team won't follow something it doesn't understand.
The category manager is accountable to management and suppliers for his pricing decisions. When faced with an unjustified price increase, he corrects it manually, and the benefit of the model disappears without anyone noticing.
An explanation turns a recommendation into an argument. That is what distinguishes predictive pricing that is actually implemented from a tool that is simply bypassed.
recommended increase in the reference price.
of this increase is attributed to the rise in competitors' prices.
low price elasticity based on historical data; the rest is due to the minimum margin rule.
Viewed this way, the price increase can be justified in committee: the category manager can accept it, limit it, or reject it with full knowledge of the facts.
Explainable, interpretable, transparent, and auditable AI
These four words are often used interchangeably. However, they answer different questions.
| Concept | Question asked | Moment |
|---|---|---|
| Explainability | Why did this result occur in this specific case? | When confirming |
| Interpretability | Can the model be understood as is, without any explanatory tools? | Choose a model |
| Transparency | What data and rules does the system use? | Live |
| Auditability | Is it possible to determine after the fact who approved what, and on what basis? | Months later |
A recommendation can be clearly explained on screen without leaving any actionable record six months later. These two requirements are verified separately, as detailed in our article on explainable and auditable pricing recommendations.
The Limits of Explainable AI
An explanation helps in making a decision. It does not guarantee that the model is correct, nor that it has identified the causes.
- Correlation does not imply causation: SHAP indicates what factored into the calculation, not what will actually drive sales.
- A rough explanation: LIME and SHAP summarize a complex model, and the two methods may yield different interpretations of the same case.
- Too much explanation undermines the explanation: twenty displayed variables are not as valuable as three clear factors expressed in the language of the field.
- A false piece of data remains false: a model based on incomplete data can be explained very well, yet it is still wrong.
That is why the explanation is no substitute for business rules or human validation. The role of each is described in our article on how an AI pricing engine works.
Explainable AI and the European AI Regulation
The AI Act tailors its requirements based on the level of risk. In pricing, the primary challenge remains getting teams to adopt the system.
The European AI Regulation (Regulation 2024/1689, known as the AI Act) imposes obligations that are proportionate to the risk posed by the systems. Article 86 grants a right to an explanation to individuals affected by a decision based on certain high-risk systems listed in Annex III.
Our belief: A pricing engine does not need a legal obligation to be accountable. A general-purpose AI that produces plausible but untraceable reasoning is not enough, as shown by our comparison between general-purpose AI and a specialized pricing solution.
Our offering pricing analytics outlines, for each recommended price, the factors that led to it. The same logic applies to our AI-powered sales forecasts: each forecast is reviewed, discussed, and validated before it is used to determine a price.
Frequently Asked Questions
Short answers to the most frequently asked questions about explainable AI.
What is explainable AI?
Explainable AI refers to the set of methods that make it possible to understand why an artificial intelligence model produces a given result. It shows which variables influenced the decision, in what way, and with what weight. It is also known as XAI, short for explainable AI. In retail, it is used to justify a price recommendation or sales forecast to the teams responsible for implementing it. Explainable AI does not make better decisions than other types of AI; rather, it makes its decisions open to discussion—and therefore verifiable and defensible.
What does XAI stand for?
XAI stands for explainable artificial intelligence. The acronym refers to a field of research and a set of tools designed to shed light on the “black box” of complex models, such as neural networks or decision tree forests. It should not be confused with the names of certain AI companies that use the same letters. In pricing, the French term “IA explicable” is more commonly used, or people refer to “explainable price recommendations.”
What is the difference between explainable AI and interpretable AI?
Interpretable AI is understandable on its own, without any additional tools: a regression model or a short decision tree can be read directly. Explainable AI may be based on a complex model, which is explained afterward using dedicated methods such as SHAP or LIME. The first approach sometimes sacrifices a little accuracy in favor of readability. The second preserves accuracy, but the explanation is only an approximation of the model. In pricing, the two approaches are often combined depending on the sensitivity of the products.
What are the main methods of explainable AI?
The main methods of explainable AI are variable importance, SHAP, LIME, and counterfactual explanations. Variable importance measures what matters to the model across all predictions. SHAP, based on Shapley values, assigns each variable its share of the contribution to a specific prediction. LIME fits a simple model around a given case. The counterfactual indicates what would have needed to be changed to obtain a different result. The choice depends on the question being asked: understanding a model or justifying a decision.
How can you tell if a pricing tool is truly explainable?
A pricing tool is truly transparent if a category manager can explain, on their own, why a particular price is recommended. The proper test is conducted using a real product from your catalog, not a pre-prepared demonstration. The factors must be presented using industry terminology—such as competitor prices, price elasticity, and margin rules—rather than statistical coefficients. It’s also important to ensure that the explanation is retained along with the decision, so it can be retrieved during an audit several months later.
Does explainable AI reduce a model's performance?
Not necessarily. Ex post explanation methods, such as SHAP or LIME, are applied to a model without altering its predictions: accuracy remains the same. The question arises especially when choosing a model that is interpretable by design—which is sometimes slightly less accurate than a complex model. In pricing, the balance often tips toward interpretability. An improvement in accuracy that isn’t implemented by the teams yields no benefit, whereas a recommendation that is understood and validated produces results on the sales floor.
What should you do when the explanation for a suggested price doesn't convince the team?
Disagreements should be treated as information, not as roadblocks. Either the explanation reveals incorrect data, a competitor’s statement that wasn’t properly reconciled, or a disrupted history—in which case the data must be corrected. Or the team is aware of a factor that the model overlooks, such as a supplier negotiation or a local event, and it must be able to make an exception while documenting it. In both cases, keeping a record of the correction and the reason for it helps improve the rules and the model over time.
Is explainable AI a requirement in pricing?
There is no general requirement for explainable AI specific to retail pricing. The European AI Regulation, known as the AI Act, scales its requirements according to the risk level of the systems, and its right to explanation applies to decisions based on certain high-risk systems listed in the annex. Explainability remains, however, a de facto requirement: senior management, auditors, and sales teams expect to be able to justify a price. For a legal analysis of a specific case, it is best to consult a specialized attorney.
Key Takeaways
- Explainable AI makes it clear why a model produces a particular result, variable by variable.
- In pricing, a local explanation for each recommended price is a prerequisite for its adoption.
- It helps with decision-making, but it does not replace reliable data, business rules, or human validation.
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A price recommendation is justifiable when the team knows, at the time of approval, what factors led to it; it is auditable when, months later, it is possible to determine who approved it and on what basis. Auditing a rule verifies a calculation, but does not explain why that price was chosen over another.
The real risk isn't the model's error; it's the fair price that no one knows how to defend and that ends up being corrected manually. For a French retailer, the log of pricing decisions is also a legal asset: it serves as proof of a reference price or a threshold for selling at a loss.
An AI-powered pricing engine prepares the data, compares your products to those of competitors, models demand, and then suggests prices filtered through safeguards. The output is a quantified recommendation, validated by the team for sensitive cases.
Its weak point is rarely the algorithm. It’s the distinction between regular prices, promotions, and stockouts in the sales history, and the quality of safeguards—including regulatory ones. A recommendation with no expected impact remains merely an opinion.
A general-purpose LLM lacks four key components required for pricing decisions: real-world connected data, business rules, impact simulation, and an auditable audit trail. It also fails to retain context from one session to the next, even though pricing is a field that relies on accumulated context.
According to the MIT NANDA 2025 study, tools deployed with a specialized publisher reach production about twice as often as those built in-house. The right division of labor leaves the exploration and drafting to the LLM and the final pricing to the specialized solution.