An associative image showing the decision-making process, in the context of which XAI justifies their logic. Image source: Unsplash

Artificial Intelligence (AI) inherently involves many technical nuances, various derivatives and forms that define its operating principles and applicability across different fields. In this article, we will explore what Explainable AI (XAI) is and where we might encounter it.

What is an XAI?

In the broadest sense, it is a technology composed of a set of various processes and methods. This set enables people to understand and trust the results produced by machine learning algorithms.

In other words, AI systems usually consist of complex mechanisms that, due to their internal code, are not accessible to the average person. In contrast, XAI places great emphasis on making the methods of AI models and the internal logic of algorithms easily interpretable and meaningful to humans.

In practice, the system is applied to the following AI model processes:

1. To describe the anticipated impact and results of the AI model.
2. To assess potential biases.
3. To identify its accuracy, fairness, and transparency.

How can XAI be Useful?

Sometimes even researchers and engineers themselves cannot understand the logic of their own algorithms when aiming for a specific result. This process allows them to comprehend how a particular AI system reached a result, to ensure that its operation meets the set expectations, to justify its logic, or to measure its value, and so on. This opens up opportunities to fully understand the system and anticipate its decision-making logic.

XAI is important because it plays a crucial role in building trust in AI integration within production processes. In general, AI operates under the so-called “black box” principle, where the information is not easily understood. This system, on the other hand, allows for easier interpretation of operational processes and promotes both technological understanding and accountability. Furthermore, organizations can use this technology to improve the user experience of a product or service.

How can we Understand How XAI Works?

Essentially, the core principle of this system’s operation involves implementing specific methods and techniques that ensure each decision made during the machine learning process can be traced and explained. Three main methods are used in these processes: prediction accuracy, traceability, and decision interpretability. The first two are meant to meet technical requirements, while the last addresses human needs.

• Prediction accuracy. This is one of the key indicators of AI success. By running simulations and comparing XAI outputs with training data results, this indicator can be determined.
• Traceability. Another the system implementation technique, for example, may involve limiting decision-making methods and setting a narrower scope for applying machine learning rules and functions. Specific traceability techniques can be used, such as DeepLIFT, which trace and display the dependencies between activated components in the system.
• Decision interpretability. This is a human-cantered technique aimed at ensuring people trust AI. In this case, when starting to work with this technology, training is conducted to help people understand how it works and why the system makes certain decisions.

Use Cases for XAI

XAI can be applied in various sectors, especially those dealing with sensitive information where decisions must be accurate and based on meaningful arguments.

We can use this technology in healthcare to speed up diagnostics, image analysis, or resource optimization — improving transparency and traceability in decision-making processes, among other benefits.

For example, when analysing a patient’s symptoms and test results, the system can not only deliver a specific diagnosis but also, for instance, highlight areas in an X-ray that show concerning patterns and explain why these suggest pneumonia rather than another respiratory disease.

It can also be applied in financial services to enhance user experience by creating a transparent loan and credit approval process. Additionally, it can help explain fraud cases — for instance, why certain transactions are flagged as suspicious. Using this technology, American Express can analyse over $1 trillion in transactions per year and understand the anomalies that triggered alerts.

In autonomous vehicles, this technology enables passengers to understand why the car suddenly brakes or changes lanes.

Overall, in these processes, AI not only provides a defined answer but also explains why a specific decision was made.

Final Word

XAI is one of the essential types of methods that enable AI systems to explain their actions in a way similar to how a person would explain their thoughts. It enhances the comprehensibility and accessibility of the technology, increases trust, and makes its application easier.

If you are interested in this topic, we suggest you check our articles:

• What is AI Infrastructure? A Detailed Guide on Key Components
• Who Created Artificial Intelligence? The AI Evolution Explained
• Agentic AI: Everything You Need to Know
• Understanding Multimodal AI

Sources: AI on Demand, IBM, SmythOS