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Enhancing Gaze-Based Interactions in Mixed Reality through Explainable AI

Core Concepts
Explainable AI (XAI) techniques can facilitate user understanding and effective adaptation of gaze-based interactions in mixed reality environments.
The content discusses the use of explainable AI (XAI) techniques to enhance user understanding and interaction with gaze-based models in mixed reality (XR) environments. The authors developed a real-time, multi-level XAI interface for a gaze-based interaction system and evaluated it in a user study. The key highlights and insights are: Gaze-based interactions in XR can leverage machine learning models to achieve higher accuracy, but the black-box nature of these models makes it challenging for users to understand and adapt their gaze behavior effectively. The authors hypothesized that XAI can serve as a bridge to help users better learn and understand AI-powered interaction systems, enabling more efficient collaboration. They developed a temporal convolutional network (TCN) model to predict the probability of target selection and used SHAP counterfactual explanations to generate multi-level XAI visualizations. A between-subjects user study with 32 participants revealed that the XAI condition led to a significant increase in selection accuracy (F1 score increase of 10.8%) compared to the control condition. Participants in the XAI condition also exhibited more nuanced and controlled gaze behavior, with lower gaze velocity and higher fixation duration, suggesting they were able to better understand and adapt their gaze to the model's behavior. Qualitative feedback from participants provided insights on their preferences for XAI explanations, including the desire for real-time, adaptive, and reinforced feedback that maps to their own gaze behavior. The findings suggest that XAI can be a valuable tool for enhancing user understanding and collaboration with model-driven gaze-based interactions in XR environments.
The average F1 score in the XAI condition was significantly (t(29) = 2.206; p < 0.05) higher (M = 0.92, SD = 0.09) than the control condition (M = 0.83, SD = 0.14). Participants in the XAI condition (M = 0.57, SD = 0.036) exhibited lower gaze velocity (t(29) = 3.13, p < 0.05) compared to those in the control group (M = 0.62, SD = 0.048). Participants in the XAI condition (M = 1.07, SD = 0.36) had significantly higher fixation duration values (t(29) = 2.17, p < 0.05) compared to the control group (M = 0.79, SD = 0.29).
"XAI can serve as a bridge to help users better learn and understand AI-powered interaction system, thus further enabling users to interact more efficiently with AI-powered system in XR." "Users expressed a desire for real-time explanations during the interaction and improved tutorials before starting to use the system." "Users want low complexity in explanations, but also control over the display of information to alter the level of complexity based on their needs, even showing multiple explanations where each provides unique insight into the system."

Deeper Inquiries

How can the XAI interface be further improved to provide more personalized and adaptive explanations based on the user's skill level and interaction patterns?

To enhance the XAI interface for personalized and adaptive explanations, several strategies can be implemented. Firstly, incorporating user profiling and skill assessment mechanisms can help tailor the explanations to individual users. By analyzing user behavior, preferences, and interaction patterns, the XAI system can adapt its explanations to match the user's skill level and learning style. Additionally, real-time feedback and reinforcement learning techniques can be integrated to dynamically adjust the level of detail and complexity in the explanations based on the user's performance and progress. Furthermore, leveraging contextual information such as the task complexity, environmental factors, and user context can enable the XAI interface to provide more relevant and situation-specific explanations. By considering these contextual cues, the system can offer timely and context-aware explanations that align with the user's current needs and goals. Implementing a feedback loop where users can provide input on the effectiveness and relevance of the explanations can also help refine and personalize the XAI interface over time.

What are the potential limitations or drawbacks of relying on XAI to enhance gaze-based interactions in XR, and how can they be addressed?

While XAI offers significant benefits in enhancing gaze-based interactions in XR, there are potential limitations and drawbacks that need to be considered. One limitation is the potential cognitive overload caused by presenting complex explanations in real-time, which can distract users from the primary task of interacting with the XR environment. To address this, the XAI interface should prioritize the delivery of concise and easily digestible explanations that do not overwhelm the user. Another drawback is the interpretability of the XAI system itself, as users may struggle to understand the explanations provided by the system. To mitigate this, the XAI interface should incorporate interactive elements such as tooltips, guided tours, and interactive visualizations to help users navigate and comprehend the explanations effectively. Additionally, providing users with control over the level of detail and type of explanations they receive can empower them to customize their interaction with the XAI system based on their preferences and cognitive abilities.

How can the insights from this study be applied to other types of model-driven interactions in XR, such as gesture-based or voice-based interactions?

The insights from this study on XAI for gaze-based interactions in XR can be extrapolated and applied to other types of model-driven interactions, such as gesture-based or voice-based interactions. Firstly, the concept of explainable interfaces can be extended to these interaction modalities to enhance user understanding and collaboration with AI-powered systems. By designing adaptive interfaces that provide real-time explanations based on user input and behavior, users can gain insights into how their gestures or voice commands are interpreted by the AI models. Moreover, the user preferences and feedback gathered from this study can inform the design of XAI interfaces for gesture-based or voice-based interactions. Understanding what types of explanations users find most effective and engaging can guide the development of tailored explanations for different interaction modalities. By incorporating user-centered design principles and iterative user testing, designers can create intuitive and user-friendly XAI interfaces that improve user performance and satisfaction across a variety of model-driven interactions in XR.