Network Operation Predictive Models for Historical Bias Mitigation
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Solution Overview
Problem
Machine learning models often perpetuate biases due to historical prejudices and social inequalities in training data, leading to discriminatory outcomes, which are challenging to identify and correct, especially in network operation decisions affecting vulnerable populations.
Innovation Solution
Implementing techniques for bias detection and mitigation during the training phase using diverse datasets and continuous monitoring, combined with explainable AI to provide transparency, and adjusting models to enhance fairness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on historical data to improve predictive accuracy, then the model's predictive performance is improved, but the model inherits and perpetuates biases present in the historical data
Solution Approach 1:
The system performs preliminary bias detection and mitigation actions during the training phase by generating inferred protected class datasets and calculating fairness metrics before the model is deployed. This proactive approach allows biases to be identified and corrected in advance, preventing discriminatory outcomes while maintaining predictive accuracy.
Solution Approach 2:
The system implements continuous feedback loops where fairness metrics are calculated based on inferred protected class attributes, and the model is retrained to improve fairness. This feedback mechanism enables the system to monitor and correct biases iteratively, balancing predictive accuracy with fairness across different demographic groups.
2Object-affected harmful factors
If fairness metrics are calculated and models are adjusted to mitigate bias, then algorithmic fairness is improved, but the complexity of the machine learning system increases
Solution Approach 1:
The system introduces an intermediary protected class demographic model that infers demographic attributes from existing user profile data without requiring direct access to sensitive information. This intermediary layer enables fairness measurement and mitigation while maintaining system simplicity and avoiding the need to store or process actual protected class data.
Solution Approach 2:
The system creates a copy or representation of protected class attributes through the inferred protected class dataset, which mirrors the structure and relationships of actual demographic data without containing sensitive information. This copying approach allows fairness analysis to be performed on a simplified representation, reducing computational complexity while preserving the essential patterns needed for bias detection.
3Reliability
If diverse and representative training datasets are used to reduce bias, then the quality and representativeness of training data is improved, but the difficulty of obtaining and processing such data increases
Solution Approach 1:
The system performs self-service by automatically generating the inferred protected class dataset from existing user profile data using the protected class demographic model. This eliminates the need for manual data collection efforts to obtain diverse demographic representations, as the system derives necessary information from already-collected user profiles through automated inference processes.
Data Source
AI summary
A network operation system and method accesses a training dataset for a network operation predictive model including historical network operation records and historical decision records, generates an inferred protected class dataset by executing a protected class demographic model, executes an algorithmic bias model using as input the historical decision records and the inferred protected class dataset to generate one or more fairness metrics, executes, based on the fairness metrics, a bias adjustment model using as input the historical decision records and the inferred protected class dataset to generate an adjusted training dataset, trains the network operation predictive model using as input the adjusted training dataset, receives an electronic request for a network operation, executes the network operation predictive model using as input at least one attribute of the electronic request for the network operation, and executes the network operation based on a prediction of the network operation predictive model.