Natural Language Map Generation for AI Model Interpretability
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Solution Overview
Problem
Non-technical users struggle to understand and trust AI and ML model predictions due to the models' complexity, as technical breakdowns are unhelpful without prior technical knowledge, leading to difficulties in decision-making and potential resource wastage.
Innovation Solution
A model mapping and enrichment system generates natural language maps that explain predictions in user-specific terminology, bridging the knowledge gap and facilitating understanding, trust, and feedback for model improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If technical breakdown of model operations is provided, then model accuracy and prediction quality are improved, but user understanding and trust deteriorate due to technical complexity
Solution Approach 1:
The patent introduces an intermediary layer that translates technical model operations into business context explanations. This intermediary converts the technical breakdown (which maintains accuracy) into non-technical language that users can understand, thereby resolving the contradiction between prediction quality and user comprehension.
Solution Approach 2:
The system changes the parameter of explanation from technical variables to business context parameters. By transforming how the model's decision-making process is presented (from technical parameters to business-relevant parameters), the system maintains accuracy while improving user understanding.
2Reliability
If detailed model explanations are provided, then user trust is improved, but time and resource consumption deteriorate
Solution Approach 1:
The system performs preliminary mapping between technical variables and business context during the model development phase. This preliminary action creates a reusable translation framework that can quickly generate explanations without requiring extensive processing time when predictions are made, thus maintaining user trust while reducing time loss.
Solution Approach 2:
The patent creates a copied or mirrored representation of the model's decision logic in business terms. This copy allows the system to provide detailed explanations rapidly by referencing the pre-established mapping rather than generating explanations from scratch each time, reducing time consumption while maintaining reliability.
3Measurement precision
If technical variables are used in model explanations, then model precision is improved, but adaptability to non-technical users deteriorates
Solution Approach 1:
The system dynamically changes the parameter of explanation based on the user's technical background. For non-technical users, it transforms technical variables into business context parameters, thereby maintaining model precision while improving adaptability to different user groups.
Solution Approach 2:
The patent applies different explanation qualities to different users based on their needs. Technical variables are used where appropriate (maintaining precision) while business context explanations are provided where needed (improving accessibility), creating a locally optimized explanation strategy for each user interaction.
Data Source
AI summary
Disclosed herein are various embodiments for training and enriching a natural language processing system. An embodiment operates by determining that a first prediction from a first machine model has been generated based on a dataset comprising a plurality of attributes. A technical map identifying a first subset of attributes of the plurality of attributes used to generate the first prediction by the first machine model is generated. Natural language translations corresponding to at least a portion of the first subset of attributes used to generate the first prediction by the first machine model are identified. A natural language map of the first subset of attributes is generated based on the natural language translations. The natural language map is provided with the first prediction.