What is Explainable AI?
Explainable Artificial Intelligence (XAI) refers to AI systems whose decisions are understandable and explainable to humans. Unlike "black box" models that only deliver results, XAI systems make transparent how they arrived at a decision.
With the increasing use of AI in critical areas such as medicine, finance, and justice, explainability is becoming increasingly important – for both ethical and regulatory reasons.
Why is Explainability Important?
Trust
People are more likely to trust decisions when they understand how they were made. A doctor who cannot explain an AI diagnosis will trust it less.
Debugging
Explainable models make it possible to identify and correct errors. When a model makes discriminatory decisions, XAI helps find the cause.
Regulation
The EU AI Act and other regulations increasingly require transparency in AI decisions, especially for high-risk applications.
Accountability
Who is responsible when an AI decision causes harm? Without explainability, this question is difficult to answer.
Explainable AI Methods
LIME (Local Interpretable Model-agnostic Explanations)
LIME explains individual predictions by creating a simplified, interpretable model for the local area around the input. It works with any model.
SHAP (SHapley Additive exPlanations)
SHAP uses game-theoretic concepts to quantify the contribution of each feature to the prediction. It provides consistent and theoretically grounded explanations.
Attention Visualization
With LLMs, you can visualize which parts of the input the model considered most strongly when generating a response. This shows what the model "pays attention to."
Chain-of-Thought
Chain-of-Thought Prompting has the model explicitly lay out its thinking process. This makes the decision-making traceable.
History of Explainable AI
The history of explainable AI spans from early research on interpretable models to the DARPA XAI program and today's EU AI Act. The following interactive timeline shows the key milestones:
History of Explainable AI
From early research to the EU AI Act
XAI Frameworks Overview
There are now numerous open-source tools and libraries that enable explainable AI. The following table shows the most important frameworks in comparison:
Challenges
- Trade-off: Explainable models are often less performant than black-box models
- Complexity: With billions of parameters, complete explainability is practically impossible
- Deceptive Explanations: Models can generate plausible but incorrect explanations
- Target Audience: An explanation for an expert differs from one for a layperson
XAI in Practice
Medicine
Diagnostic AI must be able to explain why it suggests a particular diagnosis. Doctors use this as additional information, not as a final decision.
Finance
For credit decisions, it must be traceable why an application was rejected. This is legally required in many countries.
Autonomous Driving
After an accident, it must be possible to reconstruct why the system made certain decisions.
Conclusion
Explainable AI is a key concept for the responsible use of AI. Even though complete explainability remains difficult with complex models, techniques like SHAP, LIME, and Chain-of-Thought provide important insights. With increasing regulation, XAI is moving from "nice to have" to "must have."