Decision Support Tool Explanation Method Using Observation Impact Metrics
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Solution Overview
Problem
Humans face difficulties in making decisions under uncertainty, especially when observations contradict each other, and are overwhelmed by the amount of available evidence, leading to a lack of trust in decision support tool recommendations without explanations.
Innovation Solution
A method that reorders and quantifies the impact of observation inputs on decision support tool recommendations, using metrics like Log Diagnosticity, Kullback-Leibler Distance, and Impact to provide explanations and aid decision makers in understanding the reliability of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decision support tools provide recommendations based on multiple observations, then the reliability of the recommendation improves, but the complexity of explaining the recommendation increases
Solution Approach 1:
The explanation is segmented by ordering individual observations based on their impact metric, separating the most influential observations from less influential ones. This allows the explanation to focus on key factors first, reducing cognitive load while maintaining completeness.
Solution Approach 2:
The system changes the presentation parameter of observations by sorting them according to their impact on the recommendation. This reordering transforms the explanation from a static list to a dynamic, prioritized sequence that adapts to the decision maker's need for understanding.
2Measurement precision
If the decision support tool processes more observations, then the accuracy of the recommendation improves, but the time required to explain the recommendation increases
Solution Approach 1:
The system performs preliminary computation of impact metrics for all observations before generating the explanation. This pre-processing allows the explanation to be generated efficiently by simply ordering pre-computed values rather than calculating impacts in real-time during explanation generation.
Solution Approach 2:
By segmenting observations into an ordered list based on impact, the system can present only the most relevant observations first, allowing decision makers to grasp the core reasoning quickly without needing to process all observations in detail.
3Reliability
If the decision support tool provides detailed explanations, then the trust in the recommendation improves, but the ease of operation decreases
Solution Approach 1:
The explanation provides local quality by highlighting specific observations with the highest impact metrics. Instead of treating all observations equally, the system emphasizes the most influential factors, making the explanation more targeted and easier to process.
Solution Approach 2:
Instead of presenting observations in the order they were input or processed, the system inverts the approach by ordering them by their explanatory importance. This reversal prioritizes the most relevant information for the decision maker's understanding.
Data Source
AI summary
In accordance with a particular embodiment of the invention, a method for explaining a recommendation produced by a decision support tool is disclosed. The method comprises submitting a list of observation inputs to the decision support tool and producing a recommendation. The list of inputs is then reordered according to an observation metric. The method further comprises quantifying how each input impacts the probability of the recommendation produced. The inputs may then be ranked by comparing the associated changes in probability of the recommendation produced.