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Logo Alexander Thamm

Explainable AI - Methods for explaining AI models

Explainable AI (XAI) is currently one of the most discussed topics in the field of AI. Developments such as the European Union's AI Act, which makes explainability a mandatory property of AI models in critical domains, have brought XAI into the focus of many companies developing or applying AI models. As this now applies to a large part of the economy, the demand for methods that can describe the decisions of models in a comprehensible way has increased immensely.

Why, AI? The basics of Explainable AI (XAI)

So in theory, with XAI methods and the right know-how, even complex black-box models can be at least partially interpreted. But how does it look in reality? In many projects or use cases, interpretability is an important pillar of the overall success. Security aspects, lack of trust in the model results, possible future regulations as well as ethical concerns ensure the need for interpretable ML models.

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