Machine Learning Model Bias Elimination via Case Signal Feedback
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Solution Overview
Problem
Machine learning models face challenges in reliability and accuracy due to sparse training data and human bias, leading to issues like overfitting and perpetuation of biases in classification tasks, particularly in identifying suspicious financial activities where ground-truth results are scarce and human-derived labels may introduce impartiality variations.
Innovation Solution
The system employs an end-to-end machine learning engine that utilizes case management signal data, including case closure, escalation, time on page, and comment quality metrics, to train models that reduce false positives and bias, using algorithms like true-positive models and bias eliminators to generate alerts and balance workload among case managers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using large amounts of training data to improve reliability and accuracy, then model performance improves, but data sparsity and scarcity of ground-truth results remain problematic
Solution Approach 1:
The system implements feedback loops where model predictions are continuously evaluated against actual case outcomes, and performance metrics are fed back into the training process. This allows the model to learn from its own performance patterns and improve reliability iteratively even with limited initial training data.
Solution Approach 2:
The system performs preliminary data synthesis and augmentation before formal model training, creating synthetic training examples and preparing curated datasets in advance. This preliminary action maximizes the utility of available data and reduces the impact of data sparsity on model reliability.
2Measurement precision
If human-derived labels are used to train machine learning models to improve classification accuracy, then model training becomes feasible, but human bias is introduced and perpetuated in the model
Solution Approach 1:
The system extracts and removes biased elements from training data through automated bias detection algorithms that identify and flag human-derived labels containing systematic biases. These extracted biased elements are then excluded or corrected before model training, preventing bias perpetuation while retaining useful classification signals.
Solution Approach 2:
The system changes the parameters of training data by applying fairness constraints and reweighting techniques that adjust the influence of different training examples. This transforms the training process to minimize bias impact while maintaining classification accuracy, effectively modifying how human-derived labels are utilized.
3Measurement precision
If machine learning models are trained to reduce false positives, then alert accuracy improves, but model complexity and training requirements increase
Solution Approach 1:
The system segments the alert generation process into multiple specialized models, each trained to detect specific types of suspicious activities with high precision. This segmentation allows each model to be simpler and more focused, reducing overall complexity while improving alert accuracy through ensemble evaluation of multiple specialized detectors.
4Reliability
If case management signal data is used to train models for reducing bias and false positives, then model fairness and accuracy improve, but data processing complexity and computational requirements increase
Solution Approach 1:
The system introduces intermediary processing layers that transform complex case management signal data into simplified features and representations suitable for model training. These intermediary representations preserve the fairness and accuracy signals while reducing computational complexity, acting as a bridge between raw data and model inputs.
Data Source
AI summary
The disclosure includes a system and method for obtaining, using one or more processors, case management signal data associated with one or more alerts, the case management signal data based on human interaction; training, using the one or more processors, a first model based on the case management signal data associated with the one or more alerts; and applying, using the one or more processors, the first model.