AI Annotation Generation Device Using XAI Factor Importance
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Solution Overview
Problem
Existing systems for generating annotation data do not consider important factors extracted from explainable AI (XAI), leading to suboptimal annotation selection.
Innovation Solution
A generation device that includes a processor and storage, which acquires and extracts importance levels for factors in a factor group, generates annotation information by associating specific behaviors with applicable factors, and presents these annotations to samples based on importance levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If annotation data is generated without considering XAI important factors, then the annotation generation process is simpler, but the annotation selection effectiveness deteriorates
Solution Approach 1:
The system performs preliminary extraction of important factors from XAI models before the annotation generation process. By pre-identifying which factors are most important for each sample, the system guides the subsequent annotation selection to focus on relevant behaviors, thereby improving annotation selection effectiveness without substantially increasing overall process complexity.
Solution Approach 2:
The system introduces an intermediary mechanism that connects the XAI model's important factors with the annotation generation process. This intermediary layer processes the factor importance information and uses it to selectively generate annotations, acting as a bridge between the predictive model and the annotation output to enhance reliability.
2Loss of information
If all factors in the factor group are considered for annotation generation, then comprehensive coverage is achieved, but processing time increases
Solution Approach 1:
The system extracts only the important factors from the complete factor group based on XAI analysis. By taking out and focusing on the most significant factors rather than processing all factors equally, the system maintains comprehensive coverage of relevant information while reducing the processing time required for annotation generation.
Solution Approach 2:
The system applies local quality by treating different factors differently based on their importance levels. Instead of uniform processing of all factors, the system selectively processes high-importance factors with greater detail while reducing processing for low-importance factors, thereby optimizing the balance between information coverage and processing time.
Data Source
AI summary
A generation device stores behavior information in which, for each of factors in a factor group, the factor is associated with a behavior taken when the factor is applicable. The processor executes an acquisition process of acquiring, for each of samples, a predicted probability based on whether or not each factor in the factor group is applicable and an importance level of each factor in the factor group which level is the basis for the predicted probability. An extraction process extracts a specific factor from the factor group on the basis of importance the levels obtained by the acquisition process, and a generation process acquires a specific behavior corresponding to the specific factor extracted by the extraction process, from the behavior information, and generates annotation information that presents the specific behavior to each sample to which the specific factor is applicable.