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 maximise 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 analysing X-rays or MRIs, to op­tim­ising 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 pri­or­it­ises 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 summarise 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.
• Ra­tion­al­isa­tion 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 visu­al­isa­tions, 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.