Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations
This work addresses the problem of making AI-driven insights accessible for consumers to potentially reduce electricity use, though it is incremental in applying existing XAI methods to a specific domain.
The study applied explainable AI (XAI) visualizations to electricity consumption data to provide personalized feedback, finding in an experiment with 152 participants that users could understand the patterns but required visualizations to follow known design patterns for better comprehension.
Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Knowing that specific feedback on individuals' electricity consumption triggers resource conservation, we created five visualizations with ML and XAI methods from electricity consumption time series for highly personalized feedback, considering existing domain-specific design knowledge. Our experimental evaluation with 152 participants showed that humans can assimilate the pattern displayed by XAI visualizations, but such visualizations should follow known visualization patterns to be well-understood by users.