AI Model Reliability Detection via Output Vector Variance
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Solution Overview
Problem
Machine learning models face challenges with low prediction performance for low-quality input signals and lack a reliable indication for accuracy probability.
Innovation Solution
A method is introduced to indicate the reliability of an AI model by configuring it to generate output vectors, receiving multiple output vectors, generating a matrix, extracting values in a cross section, applying a filter, calculating variance, and transmitting an indication of reliability based on a variance threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the AI model generates output values for low quality input signals, then the model processes all input data, but the prediction performance deteriorates
Solution Approach 1:
The system performs preliminary actions by generating multiple output vectors for a period of time before final evaluation. This allows the model to process input data proactively and prepare multiple predictions in advance, enabling subsequent reliability assessment without compromising processing capability.
Solution Approach 2:
The system implements feedback mechanisms by calculating variance of output values and using this information to determine reliability. The variance calculation provides feedback about the consistency of predictions, which then informs whether the output vectors are reliable or should be discarded, creating a closed-loop system that maintains both productivity and reliability.
2Device complexity
If the AI model provides output values without reliability indication, then the system is simple, but there is no reliable indication for accuracy probability
Solution Approach 1:
The system performs self-service by automatically calculating reliability metrics (variance) of its own outputs without external intervention. The model evaluates its own prediction consistency through internal variance calculation, providing reliability indications while maintaining relatively simple system architecture.
Solution Approach 2:
The system changes parameters by transforming single output values into output vectors representing multiple time points, and then calculates variance as a new parameter to indicate reliability. This parameter transformation enables reliability indication while preserving the fundamental simplicity of the AI model structure.
3Reliability
If multiple output vectors are generated for reliability assessment, then the reliability indication improves, but the computational complexity increases
Solution Approach 1:
The system segments the output into multiple output vectors, each representing output values for a specific period of time. This segmentation allows reliability assessment through variance calculation of individual segments, providing reliable indications while keeping computational complexity manageable through structured division of the output data.
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium for indicating reliability of an artificial intelligence (AI) model, comprising configuring an AI model to generate an output vector representing output values for a first period of time based on an input vector. The implementations further include receiving a plurality of output vectors from the AI model. The implementations further include generating a matrix comprising the plurality of output vectors ordered sequentially such that each output vector of the plurality of output vectors is placed in a unique row or column of the matrix. The implementations further include extracting and filtering values in a cross section of the matrix. The implementations further include calculating a variance of the filtered values. The implementations further include transmitting an indication that the plurality of output vectors is unreliable in response to determining that the variance is greater than a variance threshold.