What is Explainable AI (XAI)?

Explainable Artificial Intelligence (XAI) describes approaches and methods designed to make the decisions and outcomes of artificial intelligence (AI) comprehensible and transparent.

With the increasing complexity of AI and advances in machine learning, it has become harder for users to comprehend the processes behind AI outcomes. This makes it all the more important to maximize the understanding of AI decisions and results.

At the same time, research continues to aim for AI systems capable of learning independently and solving complex problems. This is where Explainable Artificial Intelligence (XAI) comes into play: it creates transparency by opening the AI “black box” and providing insights into how algorithms work. Without this transparency, a trustworthy foundation for digital calculations cannot be established. The transparency enabled by Explainable AI is therefore crucial for the acceptance of artificial intelligence.

The goal is to develop explainable models without compromising high learning performance. Transparency through XAI is key to building trust in AI systems. This allows users to better understand how AI works and assess its outcomes accordingly. It also helps ensure that future users can comprehend, trust, and effectively collaborate with the next generation of artificially intelligent partners. Without such traceability, it becomes challenging to ensure the reliable use and acceptance of AI.

Key Applications of XAI

Artificial intelligence is no longer limited to researchers. It is now an integral part of everyday life. Therefore, it is increasingly important that the modularity of artificial intelligence is made accessible not only to specialists and direct users but also to decision-makers. This is essential for fostering trust in the technology. As such, there is a particular obligation for accountability. Key applications include:

Autonomous driving

For example, the KI-Wissen project in Germany develops methods to integrate knowledge and explainability into deep learning models for autonomous driving. The goal is to improve data efficiency and transparency in these systems, enhancing their reliability and safety.

Medical diagnostics

In healthcare, AI is increasingly used for diagnoses and treatment recommendations, such as detecting cancer patterns in tissue samples. The Clinical Artificial Intelligence project at the Else Kröner Fresenius Center for Digital Health focuses on this. Explainable AI makes it possible to understand why a particular diagnosis was made or why a specific treatment was recommended. This is critical for building trust among patients and medical professionals in AI-driven systems.

Financial sector

In finance, AI is used for credit decisions, fraud detection, and risk assessments. XAI helps to reveal the basis of such decisions and ensures that they are ethically and legally sound. For instance, it allows affected individuals and regulatory authorities to understand why a loan was approved or denied.

Business management and leadership

For executives, understanding how AI systems work is vital, especially when they are used for strategic decisions or forecasting. XAI provides insights into algorithms, enabling informed evaluations of their outputs.

Neural network imaging

Explainable Artificial Intelligence is also applied in neural network imaging, particularly in the analysis of visual data by AI. This involves understanding how neural networks process and interpret visual information. Applications range from medical imaging, such as analyzing X-rays or MRIs, to optimizing surveillance technologies. XAI helps to decipher how AI functions and identifies the features in an image that influence decision-making. This is particularly crucial in safety-critical or ethically sensitive applications, where misinterpretations can have serious consequences.

Training military strategies

In the military sector, AI is used to develop strategies for tactical decisions or simulations. XAI plays a key role by explaining why certain tactical measures are recommended or how the AI prioritizes different scenarios.

In these and many other fields, XAI ensures that AI systems are perceived as trustworthy tools whose decisions and processes are transparent and ethically defensible.

How does XAI work?

Various methods and approaches exist to create transparency and understanding of artificial intelligence. The following paragraphs summarize the most important ones:

• Layer-wise Relevance Propagation (LRP) was first described in 2015. It is a technique used to identify the input features that contribute most significantly to the output result of a neural network.
• The Counterfactual Method involves intentionally altering data inputs (texts, images, diagrams, etc.) after a result is obtained to observe how the output changes.
• Local Interpretable Model-Agnostic Explanations (LIME) is a comprehensive explanation model. It aims to explain any machine classifier and its predictions, making the data and processes understandable even for non-specialists.
• Rationalization is a method specifically used in AI-based robots, enabling them to explain their actions autonomously.

What is the difference between explainable AI and generative AI?

Explainable AI (XAI) and generative AI (GAI) differ fundamentally in focus and objectives:

XAI focuses on making decision-making processes of AI models transparent and understandable. This is achieved through methods such as visualizations, rule-based systems, or tools like LIME and SHAP. Its emphasis is on transparency, especially in critical areas where trust and accountability are essential.

Generative AI, on the other hand, focuses on the creation of new content such as text, images, music, or videos. It employs neural networks like Generative Adversarial Networks (GANs) or transformer models to produce creative results that mimic human thinking or artistic processes. Examples include text generators like GPT or image generators like DALL-E, which are widely used in art, entertainment, and content production.

While XAI aims to explain existing AI models, GAI emphasizes generating innovative content. The two approaches can, however, be combined. For instance, generative models can be explained through XAI to ensure their outcomes are ethical, transparent, and trustworthy. Together, XAI and GAI advance transparency and innovation in artificial intelligence.