Data Science Whitepaper on Data Strategy.

Whitepaper: How to Build Trust With Explainable AI.

This whitepaper provides an overview of possible applications, advantages, methods, and challenges in employing Explainable AI in companies.

Artificial intelligence (AI) in companies is no longer a new trend. Nevertheless, the potential for AI is far from exhausted and still offers enormous opportunities. for people and AI to work together successfully, sufficient trust in AI systems is crucial. To be trustworthy, their decisions must be comprehensible, explainable, and reliable (reproducible). The increasing complexity of advanced AI systems makes it more difficult to understand how they work in detail. For users, AI systems are usually so-called black boxes. The training and architecture of modern AI systems can be technically so complex that even experts can no longer explain the system's decisions on a semantically meaningful level.

This is where Explainable Artificial Intelligence (XAI) comes in. XAI is designed to make the decisions and activities of an AI transparent and comprehensible to humans. It creates the basis for users and decision-makers to understand how AI systems work and achieve certain results. This transparency forms the basis for trust in and acceptance of artificial intelligence – the prerequisites for its successful deployment. Today, a multitude of methods make it possible to explain even complex AI systems. Even though there are several challenges to be considered, the benefits of XAI in companies are immense. This whitepaper serves as a guide to this essential future topic.

In this white paper on Data Strategy we show you:

  • What is Explainable AI (XAI), how it works and what methods are available
  • What challenges arise when using XAI and how to address them
  • How XAI creates tangible benefits for businesses

If you have any questions about this topic or about STATWORX in general, please do not hesitate to contact us.

We wish you a lot of fun while reading.


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