# Privacy-Preserving and Explainable AI in Industrial Applications
###### Abstract  
The industrial environment has gone through the fourth revolution, also called \"Industry 4.0\", where the main aspect is digitalization. Each device employed in an industrial process is connected to a network called the industrial Internet of things (IIOT). With IIOT manufacturers being capable of tracking every device, it has become easier to prevent or quickly solve failures. Specifically, the large amount of available data has allowed the use of artificial intelligence (AI) algorithms to improve industrial applications in many ways (e.g., failure detection, process optimization, and abnormality detection). Although data are abundant, their access has raised problems due to privacy concerns of manufacturers. Censoring sensitive information is not a desired approach because it negatively impacts the AI performance. To increase trust, there is also the need to understand how AI algorithms make choices, i.e., to no longer regard them as black boxes. This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research. It also presents examples related to privacy-preserving and explainable AI solutionsit is not necessary to use AI (e.g., if the problem can be solved through a mathematical formula). In the last two decades, thanks to the increase in computational power, AI has become very popular, and it has been used in several domains (medicine, marketing, industry, etc. ), with various subdomains of algorithms such as machine learning (ML) [8] and deep learning (DL). Machine learning is a popular subdomain of AI, and it is composed of statistical algorithms that can learn from data to create mathematical models for intelligent systems. Today, we have recommendation systems that use ML to suggest aspects that we like on the basis of our preferences (e.g., music, ads, and shopping). In the medical environment, we have ML models which help clinicians during diagnostic processes (decision support systems) [9]. Deep learning [10] is a widely used type of learning algorithm that relies on defining neural networks [11] with more than one hidden layer of neurons. Neural networks are inspired by the human brain, having computational units (named neurons) which are interconnected and exchange information to extract features from input data, thus enabling the mapping of input data to output data. However, neural networks used in AI do not work in the same way as human neural networks, because they exchange information using real numbers, whilst our neurons exchange information through electrical impulses. Neurons are organized in layers, where the first and the last layer are those that interact with the external environment, also named the input layer and output layer. Intermediate layers are called hidden layers. Multiple review papers have described the multitude of approaches on the basis of which AI is employed in manufacturing. In [12], [PERSON] et al. presented a theoretical framework for machine learning in manufacturing, which guides researchers in elaborating a paper in this field. They pointed to several review papers that targeted the use of ML in the industrial environment. [PERSON] et al. [13] discussed the use of AI in the context of the fourth industrial revolution. To highlight the potential advantages and potential flaws of using AI in industry, [PERSON] et al. [14] reviewed the literature and classified research on the basis of the algorithm and application domain. [PERSON] [15] also reviewed the use of machine learning in real-world applications such as cyber security, agriculture, smart cities, and healthcare. In [16], [PERSON] summarized the use of AI in different domains such as healthcare and travel. In [17], four important challenges were identified: data availability, data quality, cybersecurity and privacy preservation, and interpretability/explainability.