# _Separtial system innovation_
## 6 Research Questions  
Given the widespread claims about the adoption of AI-based solutions, both in scientific and commercial documents, public funding invested in promoting and sustaining AI solutions in the industry, and the importance of responsible use of AI in impacting applications like the process engineering ones, we, as anticipated, decided to investigate the maturity level of the use of AI and XAI, using scientific literature as a proxy of the situation of the field. We chose as signs of this maturity the spread of the use of AI or XAI in many application fields, the quality of the AI or XAI application process in the solution, the importance of the role of AI or XAI in the solutions, the presence of an explicit evaluation of the performance improvement of an AI or XAI solution for previous cases, and the adoption of XAI solutions rather than other AI solutions. To conduct our investigation of literature, we translated these criteria into 10 specific research questions, which are easier to use as specific analysis tools while studying single papers. To achieve the goals of the study, the following research questions have been defined:  
* (RQ.1) What is the approach of the paper regarding XAI? * (RQ.2) What kind of application does the paper cover? * (RQ.3) What kind of AI subset does the paper cover? * (RQ.3.1) What kind of algorithm is implemented? * (RQ.4) What kind of dataset is used for the training? * (RQ.5) Does AI play a major role in the study? Or is it an auxiliary technology? * (RQ.6) What side effect of XAI is investigated? * (RQ.7) What is the stage of XAI used method? * (RQ.7.1) What is the used technique? * (RQ.8) Is the XAI technique performance covered by the paper? * (RQ.9) Is there an assessment for improvement of performance? * (RQ.10) Are the XAI technique computational costs considered by the paper? The purpose of the first three questions is to explore whether XAI is considered in process engineering papers. In particular, RQ.1 has been designed to search for and categorize papers related to process engineering and XAI in general, specifically considering whether they follow a methodological approach, focus on implementation, or present a case study; RQ.2 has the purpose of exploring the current engagement of XAI techniques in process engineering practice, either to verify potential \"polarization\" of the AI in a specific process engineering field (i.e., design, fault diagnosis, optimization, prediction, or modeling) and to explore how the process engineering community believe XAI can be used in their fields; RQ.3 and RQ.3.1 act to verify the declination of the XAI framework through the different AI subsets. RQ.4 is used to indirectly understand the degree of penetration of XAI in process engineering practice and the current level of investment into experimental campaigns: the use of experimental data or real data implies an intervention on real setups in vivo, with the related problems in the operation of a plant, including possible interference, that is accounted as a cost paid back by the results of the experimentation; laboratory data are obtained in controlled conditions on secondary or dedicated setups with no impact on production, and denote both an intermediate level of research activity and lower investments; the use of literature data can be interpreted as an early stage of activity. It is also necessary to make some consideration about the assumption that different authors use aggregated datasets from various kinds of sources. This kind of dataset is used when the real one is not consistent or complete, so to have a descriptive dataset of the phenomena, scientists \"fill-completing\" the dataset using other sources.