User-Specific AI Explanation Generation via Domain Knowledge Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI models lack the ability to provide user-specific explanations, failing to consider the domain knowledge and expertise of users, leading to explanations that are either too technical or too general, making them incomprehensible for non-domain users and insufficient for specific decision-making.
Innovation Solution
A method and system that identify relevant features from a training dataset, distribute them into groups based on user domain knowledge levels, and generate multiple vocabularies of explanations using these features to provide tailored explanations for different user groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If AI models generate explanations using domain-specific technical terms to satisfy regulatory transparency requirements, then the explanations become more accurate and informative for domain experts, but they become incomprehensible for non-domain users
Solution Approach 1:
The patent segments the explanation generation process into multiple distinct modules: a feature extraction module that identifies relevant features from input data, a user profile analysis module that determines the user's domain knowledge level, and a vocabulary selection module that chooses appropriate terms based on the user's expertise. This segmentation allows the system to tailor explanations to different user groups without losing information completeness or comprehensibility.
Solution Approach 2:
The patent implements dynamic adaptation of explanation vocabulary based on user characteristics. The system dynamically selects from multiple pre-defined vocabularies (technical vocabulary for experts, simplified vocabulary for non-experts) based on the user's domain knowledge level. This dynamic adjustment resolves the contradiction by making the same explanatory content adaptable to different user comprehension levels while maintaining information accuracy.
2Ease of operation
If AI models provide generalized explanations to satisfy a wide range of users, then the explanations become more accessible to non-domain users, but they lose the specific details required by domain experts for improved decision-making
Solution Approach 1:
The patent applies local quality by providing different levels of explanatory detail to different user groups. For domain experts, the system selects technical vocabularies that include specific domain terms and detailed feature explanations. For non-domain users, it selects simplified vocabularies with broader, more accessible terms. This localized quality adjustment ensures each user group receives appropriately detailed explanations without compromising the other's needs.
3Device complexity
If AI models use a single vocabulary for all users to simplify the explanation system, then the device complexity is reduced, but the adaptability to different user domains and expertise levels is lost
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple vocabularies and feature sets during the model training phase, organized according to user domain knowledge levels. Instead of dynamically generating vocabulary during inference (which would increase complexity), the system prepares multiple ready-to-use vocabulary options in advance. During explanation generation, the system simply selects the appropriate pre-prepared vocabulary based on user profile matching, thus maintaining low runtime complexity while achieving high adaptability.
Data Source
AI summary
This disclosure relates to method and system for providing user-specific explanations for an output generated by an Artificial Neural Network (ANN) model. The method may include receiving a training dataset, and identifying one or more relevant features from the training dataset. The method may further include distributing the one or more relevant features into a plurality of groups. The plurality of groups may correspond to a plurality of levels of domain knowledge of users. The method may further include generating a plurality of vocabularies of explanations for an output generated by the ANN model for the training dataset corresponding to the plurality of groups, using the one or more relevant features.