Plug-and-Play Machine-Generated Noise Module for Model Debiasing
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Solution Overview
Problem
Conventional machine learning models often incorporate biases against sensitive user attributes such as race, ethnicity, gender, and disability, leading to unfair treatment and inaccurate risk assessments in decisions like loan approvals.
Innovation Solution
A plug-and-play adversarial model is introduced to de-bias predictive models by incorporating noise to reduce the influence of protected attributes, using a LightGBM model with adversarial models that operate in parallel to correct biases without modifying the original model's source code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models are used for prediction, then prediction accuracy is achieved, but biases against sensitive user attributes are introduced leading to unfair decisions
Solution Approach 1:
An adversarial model is introduced as an intermediary component that interacts with the predictive model. The adversarial model receives the same input features and generates predictions about sensitive attributes, which are then used to compute a bias metric. This intermediary mechanism enables the system to detect and correct biases without modifying the core predictive model, thus maintaining prediction accuracy while eliminating harmful biases.
Solution Approach 2:
The system implements a feedback loop where the adversarial model's predictions about sensitive attributes are fed back to the predictive model. The bias metric computed from comparing adversarial predictions with actual sensitive attributes is used to adjust the predictive model's parameters. This continuous feedback mechanism allows the system to iteratively reduce biases while preserving the model's predictive performance.
2Reliability
If adversarial models are introduced to de-bias predictive models, then fairness is improved, but device complexity increases
Solution Approach 1:
The system segments the fairness assurance function into a separate adversarial model that operates independently from the main predictive model. This segmentation allows the bias detection and correction mechanism to be added as a modular component rather than integrating complexity into the core prediction algorithm. The adversarial model handles fairness concerns while the predictive model maintains its original structure for accurate predictions.
Solution Approach 2:
The adversarial model serves multiple functions: it predicts sensitive attributes, computes bias metrics, and provides feedback for model adjustment. This multi-functionality consolidates several fairness-related operations into a single component, reducing overall system complexity compared to having separate mechanisms for each function.
Data Source
AI summary
Data features are accessed from a plurality of sources. The data features pertain to a plurality of users. The data features are inputted into a predictive model. An output is generated via the predictive model. The output of the predictive model is inputted into a plurality of adversarial models. The adversarial models include different types of protected attributes. At least some of the protected attributes are non-binary. Noise is introduced to the predictive model via each of the adversarial models of the plurality of adversarial models. The output of the predictive model is updated after the noise has been introduced to the predictive model. One or more decisions involving the plurality of users are generated at least in part via the updated output of the predictive model.