AI Screening Model Parameter Reduction for Small Data
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Solution Overview
Problem
Developing artificial intelligence expert systems for screening candidates is challenging for smaller organizations due to the need for large quantities of high-quality data, leading to over-training issues with existing methods.
Innovation Solution
A machine learning-based expert system that uses a computer-implemented screening item measuring instrument, modeling engine, and screening engine to develop a model with a limited number of parameters, requiring data from only a small number of training persons, and combines path-dependent models to effectively screen candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural net models with large numbers of parameters are used, then screening accuracy is improved, but data requirements increase and over-training occurs
Solution Approach 1:
The patent extracts only the essential parameters from the full neural net model, reducing from thousands of parameters to a limited set of M parameters. This selective extraction maintains screening accuracy while eliminating the need for large datasets, directly resolving the contradiction between accuracy and data quantity requirements
Solution Approach 2:
The patent fundamentally changes the parameter structure by limiting the number of parameters to M, where M is a small fraction of the total possible parameters. This parameter reduction transforms the model from one requiring extensive data to one that can operate with limited organizational data, solving the over-training problem
2Measurement precision
If neural net models with many parameters are used, then screening accuracy is improved, but model complexity increases
Solution Approach 1:
The patent extracts and retains only the most critical parameters needed for accurate screening, discarding the rest. This extraction reduces model complexity from thousands of parameters to a manageable M parameters, making the system feasible for small organizations while maintaining accuracy
Solution Approach 2:
The patent applies partial action by using only a subset of parameters (M parameters) rather than the complete set. This partial parameter usage is sufficient for accurate screening without the complexity burden of full neural net models, resolving the contradiction between accuracy and complexity
3Extent of automation
If existing AI screening systems are implemented, then candidate screening is automated, but data collection requirements and time increase
Solution Approach 1:
The patent changes the parameter quantity from large to limited (M parameters), which fundamentally alters the data collection requirements. This parameter change enables automated screening while reducing the time and effort needed for data collection, as the simplified model requires fewer and less complex data points
Data Source
AI summary
An artificial intelligence expert system for screening provides characteristic profiles to candidates to perform a particular task. The profiles have individual screening items within them that are expected to be related to whether or not a person is suitable for the task. The responses from the persons to the items are received by a computer implemented expert system. The expert system applies a combined model to the responses to generate a forecasted performance of the person to the task. The combined model is a linear combination of two or more path dependent regressions performed on data from a set of N training persons with known abilities to do the task. The number of parameters in each path dependent model is limited to a fraction of the number N so that the path dependent models are not over fit to the data. A suitable fraction is ⅕.