The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency

TL;DR

This paper presents a novel explainable, personalized recommendation system for energy efficiency that combines economic and ecological persuasive facts, significantly increasing user acceptance and promoting energy-saving behaviors.

Contribution

It introduces a mechanism for explainable and persuasive energy efficiency recommendations tailored to user habits, validated through real-world user feedback.

Findings

19% increase in recommendation acceptance with combined persuasive facts

Effective encouragement of energy-saving behavior through explainable recommendations

Validation via a Telegram bot study with actual data and human feedback

Abstract

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable in order to increase user trust and improve the acceptance of recommendations. In this work, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19\% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.