ELSA-Compliant Explainable Recommender Systems

Recommender systems are among the most popular artificial intelligence applications. Ensuring that recommendations not only support improving business-oriented performance indicators but also comply with existing domain-specific Ethical, Legal, and Societal Aspects (ELSA) is paramount. This article introduces an ELSA-compliant Explainable Recommender System, an architecture for implementing ELSA-compliant recommendations. Our proposed solution assumes that information regarding items, users, their interactions, and ELSA is encoded in Knowledge Graphs (KG). Given recommendations provided by a base off-the-shelf KG-based recommendation system, our system re-ranks and filters out recommendable items based on their ELSA-compliance, encoded in pre-defined domain-specific ELSA constraints. Our approach includes an explainer that leverages Large Language Models to provide explanations of the adequacy of recommended items based on ELSA. The proposed components have been assessed in the movie and animal treatment recommendation tasks. Experimental results suggest that the proposed solution effectively ensures ELSA-compliant recommendations with adequate humanized explanations in natural language to the end user.