Machine Learning Bias Detection for Fair Risk Prediction
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Solution Overview
Problem
Machine learning models often introduce unintended bias, leading to unfair predictions, particularly in risk assessment and outcome prediction tasks, which are challenging to address due to the need for fair decisions within short timeframes and the complexity of numerous predictor variables.
Innovation Solution
A method and system for detecting and reducing bias in machine learning models by calculating bias metrics for protected attributes, modifying the model structure and data, and re-training to minimize bias, using techniques such as correlation analysis and calibrated log-odds difference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are trained using traditional methods without bias detection, then training speed and simplicity are improved, but prediction fairness and accuracy deteriorate due to unintended bias
Solution Approach 1:
The patent applies preliminary action by performing bias detection and model modification during the training phase rather than after deployment. The system calculates bias metrics using protected attributes and modifies the training process to reduce bias before the model is used for predictions, ensuring fairness is built into the model from the start
Solution Approach 2:
The patent implements feedback by continuously monitoring bias metrics during training and using this information to adjust the model. The system calculates bias metrics, compares them against thresholds, and iteratively modifies the model to reduce bias, creating a closed-loop training process that improves both fairness and accuracy
2Measurement precision
If bias detection and reduction processes are implemented, then prediction fairness is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies taking out by separating the bias detection and measurement functions from the main prediction functionality. The system extracts protected attributes and calculates bias metrics as distinct operations, allowing the core prediction model to remain efficient while adding fairness checks as modular components
Solution Approach 2:
The patent uses parameter changes by modifying model parameters based on bias metric calculations. The system adjusts weights and other model parameters to reduce bias while maintaining predictive performance, optimizing the balance between fairness and accuracy through parameter optimization rather than structural changes
3Measurement precision
If comprehensive bias metrics are calculated using protected attributes, then detection accuracy is improved, but data processing requirements and time increase
Solution Approach 1:
The patent applies partial action by calculating bias metrics for the most critical protected attributes and predictor variables rather than exhaustively analyzing all possible combinations. The system identifies and focuses on the most significant sources of bias, achieving effective bias detection with reduced computational overhead
Solution Approach 2:
The patent uses preliminary action by pre-processing data to identify and flag protected attributes and predictor variables that are likely to contribute to bias before the main bias calculation process. This preliminary identification allows the system to focus computational resources on the most relevant metrics
Data Source
AI summary
In some aspects, a computing system can improve a machine learning model for risk assessment by removing or reducing bias in the machine learning model. The training process for the machine learning model can include training the machine learning model using training samples, obtaining data for a protected attribute, and calculating a bias metric using the data for the protected attribute and data obtained from the trained machine learning model. Based on the bias metric, bias associated with the machine learning model can be detected. The machine learning model can be modified based on the detected bias and re-trained. The re-trained machine learning model can be used to predict a risk indicator for a target entity. The predicted risk indicator can be transmitted to a remote computing device and be used for controlling access of the target entity to one or more interactive computing environments.