Navigation systems often aid users when traveling in unfamiliar locations. Current navigation systems tend to focus on identifying the shortest or fastest routes between two points. However, path cost expressed in terms of length and distance (i.e., the utilitarian qualities of travel) is only one attribute which could be used in route recommendation. Recent years have seen navigation systems moving beyond such utilitarian attributes. These include systems aimed at providing users with scenic, safe or attractive routes among other dimensions. In this paper, we contribute to the existing research domain by providing an overview of the different quality-aware route navigation systems that have been proposed in past research for pedestrians. In particular, we examine the different qualities which have been used as key criteria in route recommendation. As the outcome of the paper, we provide a categorization of these systems based on our proposed SWEEP taxonomy. In addition, we outline the various data sources, algorithms and evaluation approaches that have been used to implement quality-aware route navigation systems. Afterwards, we conclude by discussing potential problems encountered in previous studies and highlight promising directions for future research.

Losing a loved one through death is known to be one of the most challenging life events. To help the bereaved and their therapists monitor and better understand the factors that contribute to Prolonged Grief Disorder (PGD), we co-designed and studied a web-based explainable AI screening system named “Grief Inquiries Following Tragedy (GIFT).” We used an initial iteration of the system to collect PGD-related data from 611 participants. Using this data, we developed a model that could be used to screen and explain the different factors contributing to PGD. Our results showed that a Random Forest model using Bereavement risk and outcome features performed best in detecting PGD (AUC=0.772), with features such as a negative intepretation of grief and the ability to integrate stressful life events contributing strongly to the model. Afterwards, five grief experts were asked to provide feedback on a mock-up of the results generated by the GIFT model, and discuss the potential value of the explanatory AI model in real-world PGD care. Overall, the grief experts were generally receptive towards using such a tool in a clinical setting and acknowledged the benefit of offering a personalized result to the users based on the explainable AI model. Our results also showed that, in addition to the explainability of the model, the grief experts also preferred a more “empathetic” and “actionable” AI system, especially, when designing for patient end-users.