AI Explainability Framework for Intent Classification
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Solution Overview
Problem
Conventional AI systems lack transparency in decision-making processes, making it difficult for users to understand the reasoning behind predictions, which hampers trust and efficiency in AI-based solutions.
Innovation Solution
An explanation framework based on transformer architecture, specifically using BERT, generates visual heatmaps to explain intent classification decisions by comparing query sentences with similar sentences from a training corpus, highlighting key words and contexts for accurate processing and model improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AI intent classification models are used, then classification accuracy can be achieved, but transparency and explainability of decision-making processes deteriorate
Solution Approach 1:
The patent introduces an explanation framework as an intermediary layer between the AI classification model and the user. This framework includes an explanation generator that receives classification results and generates human-readable explanations, acting as a mediator that bridges the gap between the black-box model and transparent decision-making requirements.
Solution Approach 2:
The system segments the AI decision-making process into separable components: the classification model, the explanation generator, and the visualization module. By dividing the system into distinct segments, each can be optimized independently - the model for accuracy and the explanation module for transparency - while working together as an integrated system.
2Adaptability or versatility
If deep learning-based NLU models are used, then natural language understanding capability is improved, but comprehensibility to humans deteriorates
Solution Approach 1:
The explanation generator creates simplified copies or representations of the complex neural network decisions in human-readable format. Instead of exposing the intricate deep learning processes, the system generates explanatory texts and visualizations that replicate the essential decision logic in a form humans can easily comprehend, effectively copying the decision outcome in an interpretable format.
Solution Approach 2:
The system changes the representation parameters of the model output from complex neural activation patterns to simplified explanatory formats. The explanation generator transforms the internal model parameters into natural language descriptions and visual representations, altering how the decision information is presented to make it human-comprehensible while preserving the underlying understanding capability.
3Device complexity
If AI systems operate as black boxes, then system complexity is reduced, but trust and diagnostic capability deteriorate
Solution Approach 1:
The explanation framework provides feedback loops that allow users to review and question AI decisions. The system generates explanations that can be examined by users, and this feedback can be used to diagnose model performance issues, adjust parameters, or correct misunderstandings, thereby building trust while maintaining manageable system complexity through structured explanation generation.
Data Source
AI summary
Systems and methods for providing an explainability framework for use with AI systems are described. In one example, such an AI explainability system for intent classification uses a surrogate Bert-Siamese model approach. For example, a prediction from an intent classification model is paired with a top matching sentence and used as input to train a Bert-Siamese model for sentence similarity. Using the sentence similarity, the token/word level embedding can be extracted from attention weights of the sentences and correlations between query tokens/words, and the best matching sentences may be used for explanations.