Interactivity, Fairness and Explanations in Recommendations Full text

Giorgos Giannopoulos, George Papastefanatos, Dimitris Sacharidis, Kostas Stefanidis
In 6th International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments (HAAPIE'21) in conjunction with ACM UMAP 2021, June 21-25, 2021, Utrecht, The Netherlands
Abstract. More and more aspects of our everyday lives are influenced by automated decisions made by systems that statistically analyze traces of our activities. It is thus natural to question whether such systems are trustworthy, particularly given the opaqueness and complexity of their internal workings. In this paper, we present our ongoing work towards a framework that aims to increase trust in machine-generated recommendations by combining ideas from three separate recent research directions, namely explainability, fairness and user interactive visualization. The goal is to enable different stakeholders, with potentially varying levels of background and diverse needs, to query, understand, and fix sources of distrust.