Mental-Model Aware Explainable AI for Adaptive User Interfaces
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Solution Overview
Problem
Current intelligent user interfaces (IUIs) lack transparency, failing to consider users' knowledge and assumptions about system behavior, and often provide excessive or non-essential explanations, leading to user frustration and reduced trust.
Innovation Solution
The development of mental-model aware explainable artificial intelligence (AI) for contextual adaptive user interfaces, which uses sensitivity analyses to determine the importance of contextual variables and provides relevant, timely explanations and recommendations based on user mental models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IUIs provide detailed explanations about system models and decisions, then user trust and transparency are improved, but user frustration and task time increase due to information overload
Solution Approach 1:
The system dynamically adjusts explanation parameters based on user mental models and contextual variables. It changes the level of detail, type of information, and timing of explanations according to user knowledge state and assumptions, transforming explanations from static to adaptive parameters that balance transparency with usability
Solution Approach 2:
The explanation system is made dynamic by continuously monitoring user interactions and updating mental models. Explanations adapt in real-time based on user responses, changing from rigid pre-defined explanations to flexible, context-aware responses that adjust to user needs and prevent information overload
2Adaptability or versatility
If IUIs provide personalized experiences based on user data, then user experience relevance is improved, but device complexity increases due to need for determining user mental models and contextual variables
Solution Approach 1:
The system performs self-service by automatically inferring user mental models and contextual variables from interaction data without requiring explicit user input. It self-updates its understanding of user knowledge states and assumptions through observed behavior patterns, reducing the need for complex manual configuration
Solution Approach 2:
The system uses feedback loops where user interactions provide data to refine mental models. Explanations are tested against user responses, and the system continuously learns from feedback to improve personalization accuracy, creating a self-improving system that manages complexity through iterative refinement
3Productivity
If IUIs provide timely and relevant explanations, then user interface optimization is improved, but loss of information occurs when essential contextual details are omitted
Solution Approach 1:
The system applies local quality by providing different levels of contextual detail for different variables based on their importance to the current user and situation. Not all contextual variables are treated equally; instead, the system identifies and emphasizes locally relevant details while omitting irrelevant information, optimizing explanations for each specific context
Data Source
AI summary
A system and method for determining content to recommend to a user interface are provided. The system may determine contexts of users within environments. The system may implement a machine learning model including training data pre-trained, or trained in real-time based on historical interactions of users with data, or determined interactions with content by the users in real time. The system may analyze an item(s) of context information associated with the contexts to determine content relevant to a user associated with the system capturing content items within an environment. The system may analyze the item(s) of context information or other items of context information to determine contextual variables, of the environments, determined as relevant to the system. The system may utilize the determined content relevant to the user and the contextual variables determined as relevant to the system to determine a recommendation(s) or action(s) to present to a user interface.