Cognitive System Rationale Generation via Influence Weightage
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Solution Overview
Problem
Current question-answering systems lack the ability to provide users with a clear rationale for their answers, making it difficult for human experts to understand the reasoning behind the generated results, especially in complex problem-solving domains like differential diagnosis and decision support.
Innovation Solution
A cognitive system that generates answers for user-provided queries using analytics algorithms and determines the influence weightage of each data source, allowing for the presentation of a rationale based on these weightages, enabling users to understand how the answer was derived and facilitating adjustments to the data sources used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a cognitive system generates answers using analytics algorithms based on multiple data sources, then the answer accuracy and reliability are improved, but the user's understanding of the reasoning process deteriorates due to lack of transparency
Solution Approach 1:
The system segments the answer generation process into distinct components: the final answer, the rationale explaining the reasoning, and the influence weightages of individual data sources. This segmentation allows each component to be independently generated and presented, enabling users to understand not just what the answer is but how it was derived from specific data sources with varying levels of influence.
2Loss of information
If the system provides detailed information about data source influence weightages, then the transparency of the answer generation process is improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary rationale generation component that translates complex analytics algorithm operations into human-understandable explanations. This intermediary layer processes the raw data source influence weightages and transforms them into meaningful rationale statements, bridging the gap between complex system operations and user comprehension without requiring users to understand the underlying system complexity.
3Measurement precision
If the system processes and analyzes multiple data sources with varying influence weightages, then the answer quality is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis of data sources by pre-calculating and storing influence weightages for each data source before generating the final answer. This preliminary action allows the system to quickly retrieve and utilize pre-computed weightages during answer generation, reducing the computational burden and processing time required when users query the system, while still maintaining high answer quality through comprehensive data source analysis.
Data Source
AI summary
According to one or more embodiments of the present invention, a computer-implemented method includes generating, by a cognitive system, an answer for a user-provided query using an analytics algorithm. The answer is based on a set of data sources. The method further includes determining an influence weightage of each data source from the set of data sources. The method further includes generating and presenting a rationale for the answer based on the influence weightage.