Generative AI Information Gating for Traceable Model Outputs
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Solution Overview
Problem
Generative AI models, such as Large Language Models (LLMs), face challenges in traceability, reproducibility, and accountability due to their complex and opaque nature, leading to issues with bias and accuracy, which are difficult to address with existing model interpretability techniques.
Innovation Solution
Integrating gating devices, such as observer nodes and conjoiner, filter, and negation circuitry, into AI hardware accelerators to monitor and filter data inputs and neuronal signals, enabling detailed analysis and adjustment of AI model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are made more complex to improve content generation capabilities, then creativity and content quality improve, but traceability and accountability deteriorate
Solution Approach 1:
The patent introduces gating devices as intermediary components between input data and the generative AI model, and between different layers of the model. These gates act as mediators that monitor and control information flow, enabling traceability without reducing model complexity. The gates record which inputs contribute to which outputs, providing accountability while preserving the model's creative capabilities.
Solution Approach 2:
The patent segments the AI model into multiple layers with gating devices inserted between them. This segmentation allows independent monitoring and control of different processing stages. By dividing the complex model into manageable segments with observable boundaries, the system maintains traceability through each layer while preserving the overall complexity needed for high-quality content generation.
2Measurement precision
If existing model interpretability techniques are used to address bias and accuracy issues, then some insight is provided, but the complexity and opacity of the models make it difficult to trace specific outputs to individual inputs or internal parameters
Solution Approach 1:
Gating devices serve as intermediaries that provide precise measurement of information flow without requiring interpretation of the complex internal parameters. The gates directly observe and record which inputs activate which neurons, providing exact traceability without needing to interpret the opaque transformations within the model layers.
Solution Approach 2:
The gating devices provide feedback about which inputs contribute to which outputs, enabling continuous monitoring and adjustment. This feedback mechanism allows precise measurement of model behavior and enables targeted adjustments to address bias and accuracy issues without increasing overall model complexity.
Data Source
AI summary
Systems and techniques to increase generative artificial intelligence accountability and explainability using information gates are described herein. A prompt directed to a generative artificial intelligence (AI) model is obtained and a group of input sets in a repository, and a set operation, are identified from the prompt. This set operation is applied to a first input set and a second input set to produce an inclusion filter. The inclusion filter specifies which data from the group of input sets is included in an intermediate set. The generative AI model is then invoked on this intermediate set to produce a result.