Kashyap Todi

Xuhai Xu, Mengjie Yu, Tanya R. Jonker et al.

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses "when", "what", and "how" to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users' preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR's utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

Kashyap Todi, Ben Lafreniere, Tanya Jonker

Adaptive and intelligent user interfaces have been proposed as a critical component of a successful extended reality (XR) system. In particular, a predictive system can make inferences about a user and provide them with task-relevant recommendations or adaptations. However, we believe such adaptive interfaces should carefully consider the overall \emph{cost} of interactions to better address uncertainty of predictions. In this position paper, we discuss a computational approach to adapt XR interfaces, with the goal of improving user experience and performance. Our novel model, applied to menu selection tasks, simulates user interactions by considering both cognitive and motor costs. In contrast to greedy algorithms that adapt based on predictions alone, our model holistically accounts for costs and benefits of adaptations towards adapting the interface and providing optimal recommendations to the user.

Yi-Chi Liao, Kashyap Todi, Aditya Acharya et al.

Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human-computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., "rotating" a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.

Camille Gobert, Kashyap Todi, Gilles Bailly et al.

This paper presents SAM, a modular and extensible JavaScript framework for self-adapting menus on webpages. SAM allows control of two elementary aspects for adapting web menus: (1) the target policy, which assigns scores to menu items for adaptation, and (2) the adaptation style, which specifies how they are adapted on display. By decoupling them, SAM enables the exploration of different combinations independently. Several policies from literature are readily implemented, and paired with adaptation styles such as reordering and highlighting. The process - including user data logging - is local, offering privacy benefits and eliminating the need for server-side modifications. Researchers can use SAM to experiment adaptation policies and styles, and benchmark techniques in an ecological setting with real webpages. Practitioners can make websites self-adapting, and end-users can dynamically personalise typically static web menus.