AI Model Interpretation Through Deterministic Query Execution
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Solution Overview
Problem
AI/ML models often provide ambiguous, inaccurate, and inconsistent responses due to probabilistic processing, lacking transparency and precision, which undermines user confidence and reliability.
Innovation Solution
A computer system that integrates AI/ML models with non-AI/ML data processing systems, such as database management systems, to generate responses by providing structured code or instructions, like SQL statements, and interprets user prompts accurately, ensuring transparency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI/ML models use probabilistic processing to generate responses, then the models can handle diverse and complex queries with flexibility, but the responses become ambiguous and inconsistent, reducing reliability
Solution Approach 1:
The system segments the response generation process into two distinct parts: (1) AI/ML model generates probabilistic code or instructions, and (2) deterministic execution engine executes the code to produce final results. This segmentation allows the AI model to handle diverse queries flexibly while the execution engine ensures consistent and reliable outcomes.
Solution Approach 2:
Code or instructions serve as an intermediary between the AI/ML model and the final response. The AI model generates this intermediate representation probabilistically, but the actual response is produced through deterministic execution of the code, thereby mediating between flexibility and reliability requirements.
2Productivity
If AI/ML models process data directly to generate responses, then the models can provide comprehensive answers, but transparency and interpretability of the data processing steps are reduced
Solution Approach 1:
Instead of directly exposing the complex internal processing of the AI model, the system creates a copy or representation of the processing logic in the form of executable code or instructions. This code serves as a transparent intermediary that users can inspect to understand exactly how data is processed, while the AI model continues to generate comprehensive responses.
3Productivity
If AI/ML models access extensive data sources to improve answer quality, then the comprehensiveness of responses increases, but privacy risks and data security concerns increase
Solution Approach 1:
The system introduces an intermediary execution layer that sits between the AI model and data sources. The AI model generates code that specifies data access requirements, but actual data retrieval and processing occurs through this controlled intermediary, which can enforce privacy policies, limit data access, and secure sensitive information while still enabling comprehensive answers.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for determining and revealing interpretations of artificial intelligence models. In some implementations, a system receives a prompt from a user. The system obtains code or instructions generated by a artificial intelligence or machine learning (AI/ML) model, where the code or instructions specify criteria to retrieve data from a data source to respond to the prompt. The system generates a set of results from the data source based on the generated code or instructions, and obtains a response to the prompt that an AI/ML model generates using at least a portion of the set of results. The system also generates an interpretation statement that indicates how the prompt was interpreted by the one or more AI/ML models. The system provides output that includes (i) the response to the prompt and (i) the generated interpretation statement.