Agreement Scoring for Machine Learning Model Deployment Audits
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Solution Overview
Problem
There are concerns regarding the safety, transparency, and reliability of machine learning models, particularly in sensitive tasks such as recruitment and cybersecurity, due to issues like data bias, data drift, and lack of transparency, which can lead to unexpected behaviors and potential harm.
Innovation Solution
A method to evaluate machine learning models using agreement scores based on quantitative features of model parameters, training processes, and training datasets, providing an objective compliance score to assess transparency, bias, and reliability, and initiate automated actions for improvement or deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are deployed for sensitive tasks without comprehensive evaluation, then productivity and deployment speed are improved, but reliability and safety deteriorate due to potential bias, data drift, and unexpected behaviors
Solution Approach 1:
The patent implements preliminary evaluation of machine learning models through automated compliance scoring before deployment. The system assesses multiple characteristics including transparency, bias, data quality, and expected behavior patterns in advance, generating compliance scores that determine whether models meet safety thresholds for deployment. This preliminary action prevents unsafe models from being deployed while maintaining efficient deployment processes for compliant models.
2Reliability
If comprehensive automated evaluation of model characteristics is implemented, then reliability and transparency are improved, but device complexity and computational resources increase
Solution Approach 1:
The patent enables machine learning models to self-evaluate their own characteristics through automated compliance scoring. The evaluation system assesses transparency, bias, data quality, and expected behavior by analyzing the model's own parameters, training data, and performance metrics. This self-service approach reduces the need for complex external evaluation infrastructure while maintaining comprehensive reliability assessment.
Solution Approach 2:
The patent transforms complex qualitative assessments of model characteristics into quantitative compliance scores. By defining specific parameters for transparency, bias, data quality, and expected behavior, the system converts multidimensional evaluation into measurable metrics that can be automatically computed and compared against thresholds, simplifying the evaluation process while maintaining comprehensiveness.
3Measurement precision
If automated compliance scoring is performed on multiple model characteristics, then measurement precision of model assessment is improved, but loss of time for evaluation increases
Solution Approach 1:
The patent segments the comprehensive model evaluation into distinct characteristic assessments: transparency scoring, bias detection, data quality evaluation, and expected behavior analysis. Each segment is evaluated independently using specialized automated methods, allowing parallel processing and reducing overall evaluation time while maintaining precise measurement of each characteristic through dedicated assessment protocols.
Data Source
AI summary
Techniques are provided for evaluation of machine learning models using agreement scores. One method comprises obtaining two or more of: (i) a first set of quantitative features characterizing model parameters of a machine learning model; (ii) a second set of quantitative features characterizing a training process used to train the machine learning model; and (iii) a third set of quantitative features characterizing a training dataset used to train the machine learning model; generating a score based on an aggregation of at least portions of the two or more of the first set, the second set and the third set, wherein the score is based on an agreement of the machine learning with designated characteristics; and initiating an automated action based on the score. The automated action may comprise updating the machine learning model; generating a notification in connection with an audit; and/or selecting a machine learning model for deployment.