Fairness Simulation Framework for ML Model Bias Detection
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Solution Overview
Problem
Machine learning models trained on large text corpora are susceptible to biases, leading to fairness issues that existing tools fail to address, especially in long-term impacts on population distributions and interactions among multiple AI models.
Innovation Solution
A simulation framework that predicts the long-term impact of policies on fairness metrics for machine learning models interacting with a target population, allowing for closed-loop simulations and adjustments without sharing private data, using a central simulation system to iterate and update models based on collected metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on large text corpora to improve task performance, then the model's ability to find associations between entities and attributes is improved, but the model becomes susceptible to biases that impact fairness
Solution Approach 1:
The system performs preliminary actions by simulating the deployment of ML models and evaluating their impact on protected attributes before actual deployment. This allows biases to be detected and addressed in advance, preventing harmful biased outputs from reaching production environments.
Solution Approach 2:
A simulation environment acts as an intermediary between model training and real-world deployment. This intermediary layer allows for safe evaluation of fairness metrics and bias detection without directly impacting real populations, enabling bias mitigation while preserving model performance.
2Measurement precision
If existing fairness tools are used to detect bias in ML models, then some fairness issues can be identified, but long-term impacts on population distributions and interactions among multiple AI models cannot be addressed
Solution Approach 1:
The simulation environment dynamically models population distributions and their evolution over time as ML models are deployed. This allows the system to capture long-term fairness impacts and interactions between multiple models, moving beyond static fairness assessments to dynamic, longitudinal evaluation.
Solution Approach 2:
The simulation framework provides a universal platform that can evaluate multiple ML models simultaneously and assess various fairness metrics across different scenarios. This multi-functional approach enables comprehensive fairness evaluation that addresses both individual model biases and interactions among multiple models.
3Productivity
If ML models are deployed to interact with target populations to improve real-world performance, then the model's practical utility is improved, but private data exposure and inability to perform continuous validation occur
Solution Approach 1:
The system creates a copy of the target population within the simulation environment, allowing ML models to be tested and validated on this synthetic population. This copying approach enables continuous validation of fairness and performance without exposing or risking the use of private real-world data.
Solution Approach 2:
The simulation framework provides feedback loops that allow continuous validation and fine-tuning of ML models based on simulated fairness metrics and performance measures. This feedback mechanism enables ongoing model improvement while maintaining data privacy, as all validation occurs within the protected simulation environment.
Data Source
AI summary
Methods, systems, and computer program products for adjusting machine learning models based on simulated fairness impact are provided herein. A computer-implemented method includes obtaining, by a central simulation system, policies to be used for performing a simulation involving machine learning models, implemented on different systems, interacting with a target population; providing information for configuring simulators on the different systems, each simulator representing at least the machine learning model of a given one of the different systems; performing iterations of the simulation for the policies, wherein, for each iteration, the central simulation system: predicts a state of the target population, provides the state to the simulators, and collects metrics based on results of the simulators; and selecting and sending one of the policies to at least one of the different systems based on the collected metrics.