## 1 Introduction  
Machine learning (ML) and, especially, deep learning algorithms, as a type of artificial intelligence (AI) algorithms, are widely used in various fields, such as digital image processing [1], data analytics [2], autonomous systems [3], text and speech recognition [4], face recognition [5], robotics [6], traffic prediction [7], intrusion detection [8], etc. It is still a highly evolving field with a variety of innovative applications [9; 10]. However, when these algorithms are used in automated decision-making systems concerning individuals, several risks of a high severity occur with regard to human rights; for example, if such a system is used to decide whether a person, who is a candidate employee for a specific job, fits well with the job in question, its decision highly affects this person, and an erroneously negative response from this system yields a significant impact for the individual, posing restrictions on one's rights. More generally, errors in the output of an automated decision-making system may have serious consequences for individuals, such as [11] (a) legal effects, e.g., the denial of a particular social benefit, such as child or housing benefit, refusal of entry at the border, extensive supervision by authorities that should not have taken place, etc., and (b) other significant effects, e.g., the automatic refusal of an online credit application, intrusive profiling for various purposes, etc. Although several legal frameworks, such as the prominent General Data Protection Regulation (GPDR) in Europe [12], do not allow decisions that significantly affect individuals to rely solely on automated decision-making systems (i.e., human intervention is necessary before the final decision, even if such systems are used) (see Art. 22 of the GDPR), it is essential--even in these cases--to ensure that the underlying ML algorithms provide unbiased and accurate results to the greatest possible extent. Moreover, such systems also yield several challenging issues for the rights of privacy and personal data protection [13, 14]. Indeed, making a decision about an individual necessitates the processing of one's personal data, and this entails risks with regard to the aforementioned rights if no sufficient safeguards are in place, such as transparency of the overall processing (including information about the underlying logic involved), as well as the informed consent of individuals (although this may not be applicable when decision-making is authorized by law). Apart from this aspect, one should also consider that ML algorithms are constantly trained according to a specific training dataset; if this dataset is based on personal data, further risks occur with respect to the individuals related to these data. More precisely, it may be questionable whether these individuals are aware of this processing, as well as whether these data are used only for training and not for other purposes; additionally, these data should not be more than are needed for training the model, and this, in turn, typically necessitates the requirement that these data should not allow identification of the corresponding individuals (since such an identification is not necessary for the training purposes and would yield \"excessive\" processing). Hence, it seems that using \"anonymised\" data as the training set is a prerequisite to address such data protection and privacy risks (which could involve either direct anonymization methods, such as differential privacy (see, e.g., [15]) or other advanced cryptographic techniques that suffice to perform operations on \"hidden\" data, such as homomorphic encryption or secure computations (see, e.g., [13, 16])). However, by these means, the accuracy of the ML algorithm may be affected since \"anonymisation\" as a process is related to a type of modification of data, and thus, due to this modification, one could say that there is a trade-off between privacy and accuracy that needs to be efficiently accommodated.