Explainable AI (XAI)
Practical solutions and the interpretability of AI
Artificial intelligence (AI) is playing an increasingly significant role in our lives. From chatbots, spam filters and shopping recommendations for individuals to fraud detection for financial service providers and marketing initiatives for businesses – AI is already behind much of this. The growing prevalence of AI systems is opening up undreamt-of possibilities. However, areas of application where critical decisions are made – such as in corporate financial control or when it comes to the creditworthiness of private individuals – are under close scrutiny. The reason is the opacity of AI. In fact, studies show that many Europeans view decisions made by AI systems with unease. And this is the case even when the algorithms demonstrably deliver better results than human experts. In critical areas of application, it is important to understand how AI results are arrived at. This is where the field of Explainable AI (XAI) comes in: the research focuses on methods that make the results and functioning of AI systems comprehensible to human users.
The focus is on the following areas:
- Methods for ensuring the traceability of AI results
- User-centred design of XAI applications
#XAI #Trust #Explainability #Responsibility #Design Science #AI Made in Germany
Projects