Layered AI Model Evaluation for Transparency and Compliance
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Solution Overview
Problem
Existing AI models operate as 'black boxes,' lacking transparency and accountability, making it difficult to validate their outputs, diagnose vulnerabilities, and ensure compliance with evolving regulations and ethical standards, particularly in dynamic environments.
Innovation Solution
Constructing a layered AI model with distinct layers tailored to specific contexts and variables, enabling layer-specific model logic and responses, along with a layered prompt and evaluation system for dynamic assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models operate as black boxes with opaque algorithms, then the model complexity and processing capability are maintained, but transparency and accountability are lost
Solution Approach 1:
The patent divides the AI model into multiple distinct layers, each responsible for specific processing tasks. This segmentation allows the complex model to be analyzed layer-by-layer, providing transparency into the decision-making process while maintaining the overall processing capability of the complete model.
Solution Approach 2:
The patent introduces intermediate representations and explanations that act as mediators between the opaque model operations and the users. These intermediaries provide human-understandable insights into model decisions without requiring changes to the underlying complex model structure.
2Ease of operation
If traditional rule-based methods are used, then transparency is maintained, but the dynamic analysis capability and nuanced approach are reduced
Solution Approach 1:
The patent enables dynamic analysis by allowing the AI model to continuously adapt its processing based on input patterns while maintaining layer-specific explanations. This provides both the dynamic capability of AI and the transparency needed for understanding decisions in real-time.
Solution Approach 2:
The patent implements feedback mechanisms where model outputs are analyzed and explained back to users, creating a loop that maintains transparency. This allows users to understand model decisions while the model continues to perform dynamic analysis on new inputs.
3Reliability
If layered model structure is implemented, then transparency and accountability are improved, but the device complexity increases
Solution Approach 1:
The patent segments the model into layered structures where each layer's function and contribution can be independently analyzed. This segmentation improves accountability by allowing specific layers to be examined for their decision-making contributions while maintaining the overall model integrity.
Data Source
AI summary
Systems and methods for evaluating a pre-trained artificial intelligence (AI) model using layered inputs. The system obtains a set of application domains in which the AI model will be used, and a set of guidelines that define one or more operational boundaries of the AI model. The system determines a set of layers, where each layer is associated with corresponding guidelines and mapped to a set of variables and benchmarks. Each variable represents an attribute within the guidelines and each benchmark indicates the degree of satisfaction of the AI model with the guidelines. The AI model is dynamically evaluated against these benchmarks using a series of assessments. Subsequent assessments are dynamically constructed based on the outcomes of previous assessments. Scores are assigned to the AI model for each layer by comparing the expected and actual responses. The results are then displayed in a graphical user interface (GUI).