## 5 Discussion
### The Question of Function Allocation  
Contextualization is not only beneficial for humans but also for technical systems. For instance, it can be used for automatic failure detection and prevention [15]. Thus, the technologies described in the present article can be used to increase the level of automation, enhance machine-to-machine communication, and minimize the role of operators. In fact, keeping humans out of the loop is what many technological developments strive towards. For instance, machine learning often confronts operators with a decision but does not incorporate them in the evaluation and integration of the data that led to this decision. On the one hand, one might argue that this is a good thing, as algorithmic integration is often superior to human integration [55; 56] and the large amount of data in complex industrial systems makes automation indispensable for information processing and integration. On the other hand, suggesting specific decisions can foster an uncritical acceptance of these decisions, without cross-checking them [18; 40], and these negative effects increase with the degree of automation [174]. Although the ironies of automation were described decades ago [175], they have not made their way into the design of many real-life applications.  
A related question is what information should be available to operators to provide them with the necessary tools and knowledge to intervene if necessary. This question presents another dilemma. On the one hand, human-machine cooperation requires transparency [176; 177; 178]. When humans only receive highly constrained information, they cannot form an accurate mental model of the system. From this perspective, it might be necessary to share more information with operators instead of only making it available for internal use by the technologies. On the other hand, information overload can decrease the quality of decisions [46]. Therefore, how to provide information in ways that do not overload operators but foster productive thinking remains an important issue [5].  
Such changes, enabled by a context-aware, interconnected production system, are likely to generate benefits not only from the perspective of the production process, but also from the perspective of the human operators themselves. By allowing them to derive a more complete understanding of the technical system and the current situation, contextualization can create more meaningful jobs [179] and lead to operator empowerment [180; 181]. The resulting increases in autonomy and flexibility are likely to enhance motivation [182] and foster lifelong learning [22].  
The changes in function allocation between humans and machines that typically accompany technological advances have consequences far beyond the expected [183]. Imagine an ideal world, in which technologies are used in the most thoughtful, human-centered way: operators can inspect overviews of system relations, which are complemented with relevant and up-to date context information from different sources. Will this lead to new problems? If the presented system relations appear to be conclusive and complete, this might encourage operators to trust them and fail to notice mistakes. Similarly, explainable artificial intelligence (XAI) can lead people to over-rely on the system and comply with false explanations [156; 184; 185]. We must consider that each new technology will change the situation in unexpected ways. Therefore, it is crucial to keep people thinking. Addressing even unexpected reasoning biases may require approaches that have not been considered in human-machine interactions to date. For instance, according to the argumentative theory of reasoning [186], a powerful way to eliminate reasoning biases is to put people into an argumentative context in which they have to disconfirm the reasoning of others. Should we make operators argue with the technologies to achieve the best cooperative performance? Obviously, ideas that are based on research on human cognition must be balanced with what is feasible in a production context.