Artificial intelligence (AI) is on everyone's lips, including legislators and supervisory authorities. Therefore, there is already a wealth of legal requirements, especially from a data protection standpoint or to avoid discrimination. In all phases of an AI system, from design to feedback of results, it is necessary to go through and document with regard to all data (e.g., raw data, training data, test data, verification data) how the legal requirements are met and risks are avoided. In the case of sequential Deep Learning, each layer must be considered and evaluated individually. Assurance goals include transparency and explainability, data minimization, intervenability, availability, integrity, and confidentiality. Responsibility must be identified and communicated. In particular, the c ...