Towards improving automation with user input

Sammanfattning: As complex systems become more available, the possibility to leverage human intelligence to continuously train these systems is becoming increasingly valuable. Collecting and incorporating feedback from end-users into the system development processes could hold great potential for future development of autonomous systems, but it is not without difficulties A literature review was conducted with the aim to review and help categorize the different dynamics relevant to the act of collecting and implementing user feedback in system development processes. Practical examples of such system are commonly found in active and interactive learning systems, which were studied with a particular interest towards possible novel applications in the industrial sector. This review was complimented by an exploratory experiment, aimed at testing how system accuracy affected the feedback provided by users for a simulated people recognition system. The findings from these studies indicate that when and how feedback is given along with the context of use is of importance for the interplay between system and user. The findings are discussed in relation to current directions in machine learning and interactive learning systems. The study concludes that factors such as system criticality, the phase in which feedback is given, how feedback is given, and the user’s understanding of the learning process all have a large impact on the interactions and outcomes of the user-automation interplay. Suggestions of how to design feedback collection for increased user engagement and increased data assimilation are given.