# Privacy-Preserving and Explainable AI in Industrial Applications
## 5 Conclusions  
Artificial intelligence is widely used and brings benefits in every domain in which it is applied. Industry 4.0 has created the conditions to apply artificial intelligence through digitalization. Large quantities of data are now available to be used to generate knowledge, which is exactly what AI algorithms have been designed for. Even though it can improve industrial processes, AI needs to be applied with caution, because the access to large quantities of data also has downsides, such as the risk of data theft. To prevent this, privacy-preserving methods have been developed to be used along with AI algorithms, to ensure that knowledge is generated, while maintaining data safety. Privacy-preserving solutions proposed to date have the disadvantage of either increasing the runtime by a prohibitive amount or decreasing the accuracy significantly. Thus, the tradeoff between privacy preservation and usability is still too large. Further research is warranted to develop solutions which can be considered both secure and accurate enough. Figure 5: Predictions and attached uncertainties on a toy dataset from a squared exponential (SE) kernel (**left**) and a deep kernel learning (DKL) kernel (**right**). Outside the training points, the SE kernel model reverts to its zero mean with high uncertainty, while the DKL extrapolates confidently (adapted with permission from [77], 2021). Another aspect related to artificial intelligence is the black-box nature of the models. Increasing the accuracy means increasing the model complexity, thus making it harder to interpret how a model takes the decision starting from the given inputs. Explainable AI has been introduced to fill this gap and to help understand the way a model maps inputs to outputs. Current solutions are limited to certain models, and state-of-the-art AI approaches, such as deep neural networks, require further research to ensure levels of transparency that would allow the user to fully trust AI model decisions. As AI techniques evolve, newly developed concepts will be translated into the various application domains, including industry. Similarly, new challenges will be identified, which will need to be addressed first at a core or theoretical level, and then within the application domains. Two such current challenges were described herein: bias/fairness and robustness. AI model robustness is closely linked to AI model explainability; a robust model will perform well even when being presented with a data sample that has distinct properties from those of the training data samples. A robust AI model performs well on such out-of-distribution data, specifically because it is capable of recognizing and interpreting certain characteristics of the data sample, even if they have a slightly different appearance than in the training dataset. Such model capability is also crucial for achieving high model explainability; a model that generalizes well takes the decisions on the basis of the right characteristics of the data samples, which in turn means that it can potentially explain its decisions correctly, i.e., generating trust. Furthermore, AI model robustness is also linked to AI model bias. A model without or with low bias is likely to achieve a superior robustness by removing or at least reducing so-called 'blind spots' in the data processing and interpretation. We also note that privacy-preserving methods increase complexity, since they introduce an additional layer of data manipulation. Specifically, the data manipulation methodology itself should not introduce any bias in the data and maintain the same level of robustness as if the model was trained on the original data. Lastly, we note that privacy preservation and explainability requirements apparently have opposite effects. Privacy preservation is typically achieved by encrypting/obfuscating/altering the model input data, which in turn diminishes the explainability and interpretability capabilities.