# Privacy-Preserving and Explainable AI in Industrial Applications
###### Abstract
While the former two have been extensively discussed in the past and are well known, in this paper, we focus on topics related to the latter two challenges. AI/ML relies extensively on existing and future data to deliver accurate and reliable results. The collection of large volumes of data for centralized processing poses severe privacy concerns. Thus, the first challenge refers to the fact that, while industrial data are abundant, they are hard to circulate and access due to privacy/IP constraints, also affecting the development of computer-based solutions. Industrial AI systems are difficult to realize, as data to develop and train them exist, but are not accessible. If training datasets lack diversity, algorithms may be biased or skewed to certain types of data/events [18]. Secondly, AI algorithms should be explainable and interpretable. ML algorithms are, in general, related to the concept of 'black box', i.e., the rationale for how the outputs are inferred from the input data is unclear [19]. Algorithmic decisions should, however, ideally provide a form of explainability [20]. In general, explanations are about the attribution of the worth of input features toward the final model predictions, whereas interpretability refers to the deterministic propagation of information from the input to the response function. ML is usually regarded as a 'black box' unit; once a model is trained, its logic for determining the outputs on the basis of the inputs is not available, and further experiments and methods need to be performed to understand the way a trained model analyzes and processes the data. For stakeholders, however, it is important to understand how and why a solution is being proposed. Hence, explainable AI, with its interpretability tools, is key. Model-agnostic methods [21] were the subject of past research that yielded good results. Most of them targeted local interpretable model-agnostic explanations (LIMEs) [22] and Shapley additive explanations [23]. An important advantage of these methods is that they are compatible with a multitude of ML models. On the other hand, there are model-specific interpretation methods [24], which have the disadvantage of being compatible only with specific model types. This paper highlights the recent developments related to privacy preservation and explainability in industrial AI applications and discusses the potential impact of existing solutions in the industrial domain. Several examples are presented, related to explainable AI methods and privacy preservation techniques. Section 2 addresses aspects related to privacy preservation in industrial AI applications, while the explainability and interpretability requirements of an AI model are discussed in Section 3. In the context of the approaches described herein, Section 4 focuses on the impact of AI in industry and identifies remaining challenges. Final conclusions are drawn in Section 5. Given the focus on the two challenges, the paper should be regarded as an argumentative review; the literature is examined selectively in order to support the arguments of the necessity of both explainability and privacy preservation in industrial AI applications. Furthermore, new challenges are identified, and their interaction is discussed.