AI Explainability Metrics With Token-Aligned Attention Scores
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Solution Overview
Problem
Existing AI systems lack transparency and trustworthiness due to complex decision-making processes that users cannot fully understand, leading to mistrust and potential inaccuracies in model-based outputs.
Innovation Solution
Implement systems and methods to present AI explainability metrics, such as attention scores, to users by aligning tokens in a source document to words and combining scores to highlight relevant portions, providing a user-friendly interface for verifying model-based results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to automate tasks and generate decisions, then productivity and capability are improved, but user trust and control are reduced due to lack of explainability
Solution Approach 1:
The patent introduces an intermediary explanation layer that mediates between the AI model's internal decision-making process and the user's understanding. This layer translates complex model operations into comprehensible explanations, allowing users to verify and understand AI decisions without sacrificing automation capability. The explanation mechanism acts as a bridge that maintains both productivity and user trust.
2Adaptability or versatility
If complex AI models are deployed to handle sophisticated tasks, then task capability is improved, but system complexity and difficulty of understanding increase
Solution Approach 1:
The patent segments the complex AI system into two distinct components: the black-box model that performs sophisticated tasks and the explanation module that makes decisions interpretable. This segmentation allows the system to maintain high adaptability through advanced models while reducing perceived complexity for users through simplified explanations. The explanation component breaks down complex model operations into understandable elements.
Solution Approach 2:
The explanation module serves as an intermediary that translates complex model operations into understandable formats. It mediates between the sophisticated internal workings of the AI model and the user's need for simplicity, allowing complex capabilities to be maintained while presenting a simplified interface to users.
3Productivity
If AI models make automated decisions without explanation, then speed and efficiency are improved, but accuracy and verification capability are reduced
Solution Approach 1:
The patent implements preliminary action by generating explanations concurrently with or immediately after model predictions, rather than requiring separate verification steps. This approach maintains decision speed while providing accuracy verification, as the explanation is prepared in advance alongside the prediction itself.
Data Source
AI summary
Methods and systems for providing mechanisms for presenting artificial intelligence (AI) explainability metrics associated with model-based results are provided. In embodiments, a model is applied to a source document to generate a summary. An attention score is determined for each token of a plurality of tokens of the source document. The attention score for a token indicates a level of relevance of the token to the model-based summary. The tokens are aligned to at least one word of a plurality of words included in the source document, and the attention scores of the tokens aligned to the each word are combined to generate an overall attention score for each word of the source document. At least one word of the source document is displayed with an indication of the overall attention score associated with the at least one word.