Authors - Karen Chelentine Natalia, Trias Septyoari Putranto Abstract - Even though this is now a necessity in the sector, particularly because of COVID-19,cleanliness has taken on an even greater concern that reflects itself with the implementation of Artificial Intelligence (AI) for automated hygiene monitoring throughout hospitality environments. Yet, numerous Al models re-main black boxes and their decision-making process is opaque to operators - a dynamic that can undermine trust in the technology. Bhalearo et al.: Evaluation of Explainable Artificial Intelligence (XAI) in Cleanliness Detection Systems: A Study on Hospitality Services This study used a qualitative case study method with in-depth interviews of four participants: a manager, supervisor, staff member and hotel guest as data collector. The findings suggest Al can aid efficiency and consistency in cleanliness monitoring, but that limited interpretability could also hamper their trustworthiness. XAI is an excellent way to increasing the understanding of system outputs by users, and this leads to increased trust and ac-countability through a better explanation - especially with visual explanations like heatmaps. We highlight the benefits of technological efficiency but also transparency, especially in hospitality management.
