Explainable AI Model Using Visual Interpretation
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Solution Overview
Problem
Existing models, such as AI/ML models, often generate outputs without providing explainable reasoning, making it difficult for entities using these models to assess their reliability and accuracy.
Innovation Solution
The implementation of an explanation model that is trained to associate input data with explanation labels, allowing for the generation of visual representations and explanations of model outputs, thereby making the model explainable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are used to generate outputs, then productivity and prediction accuracy are improved, but model explainability and trust reliability deteriorate
Solution Approach 1:
An explanation model is introduced as an intermediary component between the original AI/ML model and the user. This explanation model receives inputs, generates visual representations, and provides explanation labels that interpret the original model's outputs, thereby bridging the gap between complex model predictions and user understanding without affecting the original model's productivity
Solution Approach 2:
The explanation model creates visual representations and explanation labels that copy and reinterpret the original model's output in a human-understandable format. Instead of directly exposing the complex internal logic of the original model, the explanation model generates simplified visual and textual descriptions that convey the same predictive information
2Measurement precision
If complex AI/ML models are used, then measurement precision and prediction accuracy are improved, but device complexity and algorithm opacity worsen
Solution Approach 1:
The explanation model extracts only the essential information needed for interpretation from the complex original model's output. It takes the input data and the original model's prediction, then extracts and presents only the relevant explanation labels and visual representations, separating the explanatory function from the complex computational function
Solution Approach 2:
The system is segmented into two distinct models: the original AI/ML model that handles complex pattern recognition and prediction, and the explanation model that handles visualization and interpretation. This segmentation allows each model to be optimized for its specific function, with the explanation model providing simplified outputs regardless of the original model's complexity
Data Source
AI summary
A system described herein may train an explanation model based on a set of images and a set of explanation labels. The system may receive input data, and may provide the input data to the explanation model and a second model. The second model may provide a set of output labels, which may include performing unknown or “black box” processing on the input data. The explanation model may generate one or more images based on the input data, compare the images to the set of images based on which the explanation model was trained, and accordingly identify one or more explanation labels with bounding boxes associated with the generated one or more images. The system may output, in response to the input data, the set of output labels provided by the second model as well as the identified explanation labels.