ML Bias Filter Gate Model Classification Accuracy
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Solution Overview
Problem
Existing computer-based systems face challenges in accurately classifying data inputs into bias categories, often leading to misclassifications that can be offensive or damaging, particularly in contexts like credit risk assessment, where biases based on gender, race, or accent can result in incorrect judgments.
Innovation Solution
A system utilizing a classification machine learning model, a bias filter machine learning model, and a gate machine learning model to determine the confidence probabilities of classification outcomes and manage bias, where the gate model decides whether to output the classification based on predefined thresholds, thereby preventing potentially biased outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a classification machine learning model is used to classify data inputs into categories, then classification speed and automation are improved, but classification accuracy and reliability deteriorate due to bias-based misclassifications
Solution Approach 1:
A bias filter machine learning model is introduced as an intermediary component between the classification model and the output. This bias filter analyzes the classification outcomes and identifies potential biases before final classification is made, thereby maintaining high classification speed while improving accuracy by catching biased misclassifications that the primary classification model alone would produce
2Reliability
If bias filtering mechanisms are added to improve classification accuracy, then classification accuracy is improved, but system complexity increases
Solution Approach 1:
The classification system is segmented into distinct functional components: a classification machine learning model for primary categorization and a bias filter machine learning model for bias detection. This segmentation allows each component to specialize in its specific function, improving overall accuracy while making the system more manageable and interpretable despite the added complexity
3Reliability
If multiple machine learning models are deployed to filter bias, then classification accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The bias filter model applies partial action by not requiring complete reclassification of all data inputs. Instead, it focuses computational resources selectively on identifying and filtering biased classifications, thereby reducing the overall computational burden compared to running multiple full classification models while still achieving improved bias detection accuracy
Data Source
AI summary
A method includes receiving, by a processor, bias data categories. A data input from a user for classification in data categories is received. A classification machine learning model is utilized to classify the data input in at least one data category and determine a first confidence probability in a classification outcome. A bias filter machine learning model is utilized to determine a second confidence probability that the classification outcome of classifying the data input into the at least one data category is based on at least one bias characteristic associated with at least one bias data category. A gate machine learning model is utilized to determine when to output the classification outcome of classifying the data input into the at least one data category to a computing device of a user based at least in part on the first confidence probability, the second confidence probability, and a predefined bias threshold.