Learning Classifier Systems for Self-Explaining Socio-Technical-Systems
This work addresses the need for explainable AI to improve operator acceptance and efficiency in socio-technical systems, though it is incremental as it applies existing LCS methods to a specific domain.
The paper tackles the problem of making decision support systems in socio-technical settings more transparent by proposing Learning Classifier Systems (LCS) for self-explaining capabilities, and it demonstrates this through a case study in manufacturing that yielded useful insights for model design and stakeholder engagement.
In socio-technical settings, operators are increasingly assisted by decision support systems. By employing these, important properties of socio-technical systems such as self-adaptation and self-optimization are expected to improve further. To be accepted by and engage efficiently with operators, decision support systems need to be able to provide explanations regarding the reasoning behind specific decisions. In this paper, we propose the usage of Learning Classifier Systems, a family of rule-based machine learning methods, to facilitate transparent decision making and highlight some techniques to improve that. We then present a template of seven questions to assess application-specific explainability needs and demonstrate their usage in an interview-based case study for a manufacturing scenario. We find that the answers received did yield useful insights for a well-designed LCS model and requirements to have stakeholders actively engage with an intelligent agent.