Semantic Grouping for Interpretable Predictive Models
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Solution Overview
Problem
Predictive models are often considered 'black-box' systems, making it difficult for users to understand the reasoning behind their outputs, as they do not provide transparent interpretations of the results.
Innovation Solution
The implementation of semantic grouping techniques, which involve processing current data using a predictive model, determining syntactically similar sub-models, merging nodes using a domain ontology, and providing human-readable interpretations to explain the results, thereby making the predictive model more transparent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predictive model is used to provide output based on input data, then the predictive capability is improved, but the interpretability and transparency of the model deteriorates
Solution Approach 1:
The patent introduces an intermediary interpretation layer that sits between the predictive model and the user. This layer includes components such as a rule extractor that derives human-readable rules from the model's predictions, a rationale generator that explains why specific predictions were made, and a confidence indicator that communicates the model's certainty. These intermediaries translate the black-box model outputs into comprehensible information without altering the model's predictive functionality.
Solution Approach 2:
The patent segments the predictive model into multiple interpretable components. Instead of presenting the model as a monolithic black box, it breaks down the prediction process into distinct elements such as individual rules, feature contributions, and decision pathways. Each segment can be independently analyzed and explained, allowing users to understand specific aspects of the model's reasoning while maintaining the overall predictive capability.
2Productivity
If a black-box predictive model is used, then the processing speed and productivity are improved, but the transparency and understanding of the reasoning process deteriorates
Solution Approach 1:
The patent applies partial action by providing interpretations selectively rather than for every single prediction. The system can adjust the level of interpretation detail based on user needs, data characteristics, and contextual factors. This allows the system to maintain high processing speed for routine predictions while providing detailed transparency when needed, thus balancing productivity and ease of operation without requiring full interpretation of every model output.
Data Source
AI summary
Implementations are directed to receiving current data, processing the current data using a predictive model to provide a result, the result corresponding to a sub-model of the predictive model, determining a set of syntactically similar sub-models based on other data, providing at least one semantic model based on the sub-model of the predictive model, one or more syntactically similar sub-models of the set of syntactically similar sub-models, a domain ontology (knowledge graph), and constraints, the at least one semantic model being provided by merging nodes of the sub-model of the predictive model, and a previously determined sub-model of the predictive model using the domain ontology, a label of the domain ontology being used to label a merged node, determining an interpretation based on the at least one semantic model, the interpretation providing at least one reason for the result, and providing the interpretation.