Abstract

Deep learning still has drawbacks in terms of trustworthiness, which describes a comprehensible, fair, safe, and reliable method. To mitigate the potential risk of AI, clear obligations associated to trustworthiness have been proposed via regulatory guidelines, e.g., in the European AI Act. Therefore, a central question is to what extent trustworthy deep learning can be realized. Establishing the described properties constituting trustworthiness requires that the factors influencing an algorithmic computation can be retraced, i.e., the algorithmic implementation is transparent. We derive a mathematical framework which enables us to analyze whether a transparent implementation in a computing model is feasible. Finally, we exemplarily apply our trustworthiness framework to analyze deep learning approaches for inverse problems in digital computing models represented by Turing machines.