AI Recommendation Explanation System Using Augmented Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Artificial intelligence systems face challenges in providing explanations for their recommendations, leading to reduced user trust due to inherent biases and the complexity of understanding AI models, especially in non-technical domains.
Innovation Solution
A system that combines feature vectors with expert-provided explanations to generate augmented labels, allowing a classifier to analyze real-world data and decompose labels to provide explanations matching user complexity and domain, thereby increasing explanation accuracy and reducing bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI systems provide recommendations using complex models, then recommendation accuracy is improved, but explanation understandability deteriorates
Solution Approach 1:
The patent segments the explanation generation process into multiple components: (1) generating initial explanations from the complex AI model, (2) evaluating explanation quality against multiple criteria, and (3) iteratively refining explanations to improve understandability while preserving accuracy. This segmentation allows the system to maintain recommendation accuracy while improving explanation clarity through systematic refinement.
Solution Approach 2:
The patent introduces an intermediary explanation evaluation and refinement module that acts as a mediator between the complex AI recommendation model and the end user. This intermediary component translates complex model outputs into understandable explanations by applying evaluation criteria and iterative refinement, thereby bridging the gap between accurate recommendations and comprehensible explanations.
2Reliability
If AI systems use sophisticated algorithms to reduce bias, then decision fairness is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by incorporating bias mitigation strategies during the training phase of the AI model. By pre-processing training data and applying fairness constraints before the model is deployed, the system reduces bias in recommendations without requiring complex real-time adjustments during operation, thereby improving decision fairness while controlling system complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the explanation generation and evaluation process provides information back to the recommendation model. This feedback loop allows the system to identify and correct biased decisions by evaluating whether generated explanations adequately represent the decision rationale, thereby improving decision fairness through iterative refinement rather than requiring overly complex algorithms.
3Reliability
If AI models are made more interpretable, then user trust is improved, but model performance may deteriorate
Solution Approach 1:
The patent uses copying by creating simplified representations or proxy models that replicate the behavior of complex AI models for explanation purposes. Instead of replacing the high-performance complex model with a simpler interpretable one, the system generates copies or approximations that capture the essential decision logic, thereby maintaining model performance while providing interpretable explanations that build user trust.
Solution Approach 2:
The patent introduces an intermediary layer that sits between the complex AI model and the user, which generates and refines explanations without modifying the underlying model. This intermediary component preserves the high performance of the original complex model while translating its outputs into trustworthy, understandable explanations through evaluation and iterative refinement, thus maintaining model performance while improving user trust.
Data Source
AI summary
Techniques regarding explanations for artificial intelligence recommendations are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include: a combination component that receives a first training dataset comprising first feature vectors, first classes and first explanations, and combines the first classes and the first explanations to produce first augmented labels and a second training dataset that comprises the first feature vectors and the first augmented labels; a classifier, trained on the second training dataset, that analyses second feature vectors and generates second augmented labels; and a decomposing component that decomposes the second augmented labels, using the classifier, to generate second classes and second explanations.