Bayesian Hyperparameter Optimization with Fairness Constraints
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Solution Overview
Problem
Existing machine learning systems face challenges in optimizing hyperparameters for fairness and accuracy due to the complexity of performance metrics and the need to consider model-specific bias and accuracy constraints, which can lead to unintended unfair decisions.
Innovation Solution
The implementation of Bayesian optimization techniques with constrained expected improvement search, using probabilistic models to automatically determine optimal hyperparameters that satisfy fairness constraints, such as Equal Opportunity and Equalized Odds definitions, to ensure fair and accurate machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized algorithmic fairness techniques are employed to mitigate encoded biases, then fairness is improved, but the applicability is limited to a single family of ML models and a specific definition of fairness
Solution Approach 1:
The patent implements a universal hyperparameter optimization framework that can handle multiple fairness definitions (equal opportunity, equalized odds, demographic parity) and multiple ML model families simultaneously. The system uses a configurable fairness constraint module that accepts different fairness metrics as parameters, allowing the same optimization pipeline to adapt to various fairness requirements without requiring model-specific implementations.
2Reliability
If hyperparameter optimization is performed to develop accurate and fair ML models, then model performance is improved, but the evaluation process becomes costly and expensive
Solution Approach 1:
The patent employs surrogate models (proxies) that approximate the expensive fairness and accuracy evaluation functions. These surrogate models are trained on a subset of hyperparameter configurations and then used to predict performance metrics for unevaluated configurations, eliminating the need to perform costly evaluations for every candidate in the search space.
Solution Approach 2:
The system creates simplified copies or approximations of the expensive evaluation processes through surrogate models. Instead of running full ML training and evaluation pipelines for each hyperparameter candidate, the system uses these copied evaluation functions that provide approximate but sufficiently accurate predictions at a fraction of the computational cost.
3Adaptability or versatility
If multiple fairness definitions and model-specific constraints are considered during optimization, then fairness coverage is improved, but the optimization process becomes more complex
Solution Approach 1:
The patent separates the complexity of handling multiple fairness definitions into modular, independent constraint modules. Each fairness definition (equal opportunity, equalized odds, demographic parity) is implemented as a separate constraint function that can be independently configured and evaluated. This segmentation allows the optimization system to handle multiple fairness requirements without increasing overall system complexity, as each constraint can be applied independently through the unified hyperparameter optimization framework.
Data Source
AI summary
Hyperparameters for tuning a machine learning system may be optimized for fairness using Bayesian optimization with constraints for accuracy and bias. Hyperparameter optimization may be performed for a received training set and received accuracy and fairness constraints. Respective probabilistic models for accuracy and bias of the machine learning system may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing constrained expected improvement on the respective models, training the machine learning system using the identified values to determine measures of accuracy and bias, and updating the respective models using the determined measures.