Trustworthy artificial intelligence: Comprehensible, transparent and correctable

Abstract

With the digital transformation, artificial intelligence (AI) applications are also finding their way into more and more areas of work and life. In particular, models learned from data are being used, which are mostly opaque black boxes. The fact that people can understand why an AI system behaves the way it does is necessary for various reasons: The model developers themselves must be able to assess properties of the learned models-in particular, possible biases due to overfitting to the data used for learning. For safety-critical applications, aspects of certification and testing are also becoming increasingly relevant. Domain experts- for example, in medical diagnostics or quality control in industrial production-must be able to comprehend, verify and, if necessary, correct system decisions. Consumers should understand why a system-a smart home control, a driving assistance-behaves in a certain way and why they are recommended certain products, offered certain tariffs or denied certain offers. After a brief introduction to the topic of AI, the chapter gives an overview of methods of the so-called third wave of AI. Central to this are approaches of explainable AI (XAI), which are intended to make the decisions of AI systems comprehensible. The main approaches are characterized and shown for which objectives and applications they are suitable in each case. It is shown that in addition to the highly regarded methods for visualization, methods that allow system decisions to be described in a differentiated manner are also particularly important. It is also argued that, in addition to comprehensibility, interactivity and correctability of AI systems are necessary so that AI systems do not restrict human competences but support them in partnership.