Artificial Intelligence with a user manual: what is Explainable AI and where is it applied?

Artificial Intelligence is increasingly effective in supporting companies with forecasting, optimization, and decision automation within organizations. Although AI models can accurately answer questions or predict outcomes, they often fail to explain why they arrived at a given result. As a result, users see only a number – without context, justification, or the ability to ask follow-up questions.

In such cases, Explainable AI (XAI) plays a crucial role – an approach that makes it possible to understand how a model works, clarify its decisions, and rely on them with confidence. In this summary, we show how XAI operates and how it translates into real business value, particularly in predictive applications.

What is Explainable AI (XAI)?

Explainable AI (XAI) refers to a set of methods and techniques that enable understanding how Artificial Intelligence models work, and in particular – explaining why a given model made a specific decision. Unlike so-called “black box” models, XAI allows for transparency and interpretability of results.

Key goals of XAI include:

• Identifying the impact of individual input variables on the model’s outcome,
• Detecting decision biases and artifacts,
• Increasing trust among end users,
• Meeting regulatory requirements (e.g., AI Act, GDPR),
• Enabling auditing of algorithms and their operation in production environments.

Example areas of Explainable AI applications across industries

Explainable AI (XAI) today forms the foundation of trust, auditability, and compliance of Artificial Intelligence models in environments where algorithmic decisions have a significant operational, financial, or legal impact. In many industries, explainability is no longer seen as an optional add-on or an efficiency booster – but as a mandatory criterion for deploying models into production.

• Industry and manufacturing

In the context of predictive maintenance, sensor fleet management, or production quality analysis, XAI provides insight into why and based on which key features a model predicts an approaching failure. This allows not only for quicker response but also ensures that maintenance activities remain compliant with engineering procedures and safety requirements.

• Healthcare

In clinical applications, such as diagnostics based on medical imaging, genomic sequence analysis, or therapeutic recommendations, model explainability becomes a condition for acceptance by medical communities and regulators. Transparent justification of a model’s decision (e.g., highlighting a pathological area on an image) enables physicians to verify prediction accuracy and minimizes the risk of diagnostic errors.

• Trade and logistics

In systems used for demand forecasting, customer segmentation, offer personalization, or supply chain optimization, explainability makes it clear which factors – seasonal, promotional, or geographic – actually influence model recommendations. This allows operations teams to make data-driven planning decisions where the underlying dependencies are understandable and open to scrutiny.

• Finance, banking, insurance

In areas such as credit scoring, insurance risk assessment, or fraud detection, AI models make decisions with significant consequences for customers and institutions. Explainable AI not only makes it possible to provide transparent justifications for decisions – such as loan denials or risk evaluations – but also helps ensure compliance with legal regulations (e.g., EBA guidelines or Article 22 of GDPR, which guarantees the right to a “logical explanation” of decisions made automatically).

Additionally, XAI makes it possible to identify and reduce the risk of algorithmic discrimination, ensuring that no protected group is treated unfairly, while also building stakeholder trust through transparent model operations.

• Public sector

In public administration, the role of XAI grows alongside the increasing automation of decision-making. The transparency of AI models supports the fight against unfair decision-making criteria, enables the detection of systemic biases, and ensures full compliance with anti-discrimination laws and personal data protection regulations.

Example – how explainability works in forecasting

Imagine a system predicting the sales of a specific product over the coming weeks. The model forecasts a 15% drop in demand. Thanks to XAI mechanisms, the system additionally calculates the contribution of individual factors to this forecast, for example:

• the end of a promotional campaign reduced the forecast value by eight percentage points
• seasonally lower demand in the same period of previous years reduced the forecast value by four percentage points
• an increase in competitors’ market share in the analyzed segment reduced the forecast value by three percentage points

Based on these values, the system can automatically generate an interpretation: “The projected 15% drop results from the end of a promotional campaign, seasonally lower demand observed in previous years, and the growing market share of competitors in the analyzed segment.”

Popular techniques for model explainability

To implement explainability in predictive models, various approaches are used depending on the type of model, its complexity, and end-user requirements. Examples include:

• SHAP (Shapley Additive exPlanations)

Based on Shapley values from game theory, this method assigns each variable a share in the final prediction. It works both globally (showing how the model generally treats individual variables) and locally (interpreting a single outcome).

• LIME (Local Interpretable Model-Agnostic Explanations)

Creates a simplified, interpretable model (e.g., linear) for each individual prediction, which locally approximates the behavior of the “black box.” This helps understand which features had the greatest impact on the forecast.

• Ceteris Paribus (CP)

A local analysis method that calculates how the model’s prediction changes for a single observation depending on one selected variable, assuming all other features remain unchanged. CP makes it possible to understand how the model “locally” reacts to a given variable, which supports the interpretation of specific model decisions.

XAI Interface

In production environments, effective implementation of Explainable AI requires not only generating explanations but also presenting them in a way that is accessible to end users. A growing trend is the integration of XAI components with conversational interfaces, based on large language models (LLMs), enabling interactive analysis of model outcomes.

With such a solution, a user can ask questions in natural language – e.g., “Why did the demand forecast change?” or “Which factors had the greatest impact on the result?” – and the system responds with answers based on actual input data and model structure.

This type of interface also supports scenario analysis (“What would happen if we extended the promotion?”), presenting the consequences of alternative decisions. The conversational layer acts as a “translator” between a complex model and a non-technical user, allowing for fuller understanding and control over the predictive system.

A well-designed XAI interface not only increases model transparency but also supports decision-making processes by shortening analysis time, boosting trust, and minimizing the risk of misinterpretation.

Explainable AI as a key element

Explainable AI is becoming a crucial component of mature artificial intelligence systems – supporting transparency, enabling the interpretation of model decisions, and strengthening user trust. In predictive applications, such as forecasting, it helps better understand predictions and make more accurate decisions.

“Explainable AI is not an add-on, but a foundation – our models must be not only accurate but also understandable and reliable for users. That’s why we dedicate special attention to this area within our Data Science competencies.”

Jarek Tkocz

Chief Data Science