Reinforcement Learning Agent Bias Reduction via Reward Feedback
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Solution Overview
Problem
Existing AI models often amplify biases present in input data, leading to unfair predictions and outcomes, as they lack effective methods to mitigate latent biases and ensure data representation across all feature combinations.
Innovation Solution
A reinforcement learning-based system that computes reward values based on similarity between pre-trained model predictions and agent predictions, applying rules with weights and comparison thresholds to identify and penalize biased outcomes, thereby reducing biases while maintaining prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained with comprehensive input data to improve prediction accuracy, then the model can generate more accurate results, but the model amplifies biases present in the input data leading to unfair predictions
Solution Approach 1:
The patent implements feedback mechanisms where the model's predictions are continuously evaluated against fairness metrics and bias detection algorithms. The system provides feedback signals that adjust model parameters to reduce bias while maintaining accuracy, creating a closed-loop system that iteratively improves both fairness and precision
Solution Approach 2:
The patent changes key parameters including data sampling strategies, feature selection criteria, and model training parameters to reduce bias. Specifically, it modifies the training process by introducing fairness constraints as additional parameters in the optimization function, allowing the model to balance accuracy and fairness through parameter adjustment
2Object-affected harmful factors
If variables associated with bias are explicitly removed from the model to reduce bias, then fairness improves, but the model cannot prevent bias encoding through other correlated variables
Solution Approach 1:
The patent introduces intermediary components including fairness auditors, bias detection layers, and correlation analysis modules that act as mediators between the input data and model predictions. These intermediaries monitor and adjust for bias encoding through latent variables without removing the underlying features, maintaining reliability while reducing harmful bias
3Measurement precision
If more input data is collected to improve representation across all feature combinations, then model accuracy improves, but data collection becomes increasingly difficult and costly
Solution Approach 1:
The patent performs preliminary actions by using synthetic data generation and data augmentation techniques to create representative samples before actual data collection. It pre-processes available data to identify and correct representation gaps, and uses generative models to synthesize underrepresented feature combinations, reducing the need for extensive additional data collection
Data Source
AI summary
Embodiments provide methods and systems for reducing bias in an artificial intelligence model. A method includes computing, by a processor, a reward value based at least in part on a similarity between model predictions from a pre-trained model and agent predictions from a Reinforcement Learning (RL) agent. The method includes performing each step of one or more steps of a rule of a plurality of rules. The rule is assigned a weight and the rule includes a protected attribute, a cumulative statistic value type, and a comparison threshold. The method includes sending a cumulative reward value generated using the reward value and each weighted punishment value computed based at least in part on applying each rule of the plurality of rules to the RL agent. The RL agent learns to biases from the agent predictions while maintaining similarity with model predictions by maximizing the cumulative reward value.