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Predicting the Fairness of Machine Learning Software Configurations

Core Concepts
The core message of this article is that machine learning regression models, particularly tree-based algorithms like Tree Regressor and XGBoost, can accurately predict the fairness of hyperparameter configurations for various machine learning training algorithms across different fairness-sensitive datasets. The authors also investigate the robustness of these prediction models under temporal distribution shifts in the test data.
The article investigates the relationships between hyperparameters of machine learning (ML) algorithms and the fairness of the resulting models. The authors focus on group fairness notions, specifically the average odd difference (AOD), and explore the hyperparameter space of 5 popular ML training algorithms (Decision Tree, Logistic Regression, Support Vector Machine, Random Forest, and Discriminant Analysis) across 4 fairness-sensitive datasets (Adult Census, Compas Recidivism, German Credit, and Bank Marketing). The authors first use an evolutionary search algorithm to generate a dataset of hyperparameter configurations and their corresponding fairness (AOD) values. They then train four different ML regression models (Deep Neural Network, Support Vector Regressor, Tree Regressor, and XGBoost) to learn a function that can predict the fairness of hyperparameter configurations. The results show that Tree Regressor and XGBoost significantly outperform Deep Neural Networks and Support Vector Regressors in accurately predicting the fairness of hyperparameters, with 40% of the cases achieving an R^2 score of 0.95 or higher. However, the precision of the predictions depends on the ML training algorithm, dataset, and protected attribute. Under temporal distribution shifts (e.g., training on 2014 data and predicting for 2015), the Tree Regressor and XGBoost models maintain reasonable accuracy in 20% of the benchmarks, particularly for the hyperparameters of Logistic Regression and Discriminant Analysis with sex as the protected attribute. The precision is significantly degraded for other training algorithms and protected attributes like race. The authors conclude that their approach provides a sound framework to systematically examine the influence of hyperparameters on fairness and can help reduce the cost of training fair data-driven software solutions by avoiding biased configurations and leveraging promising hyperparameters. They also highlight the challenges in making such predictions in general and point out the circumstances for successful usage and future research directions.
"The average of differences between the true positive rates and the false positive rates of two protected groups (e.g., male vs. female) is the average odd difference (AOD)." "The difference between the true positive rates of two protected groups is the equal opportunity difference (EOD)."
"Fairway [8] searched the space of HPs to mitigate software discrimination. Parfait-ML [40] found and localized discriminatory HPs in the prevalent ML algorithms." "When there is one year shift (e.g., trained over the income census 2014 and predicting for 2015), Tree Regressor and XGBoost achieved high accuracy in 20% of benchmarks. We observed that these cases are related to the HP space of Logistic Regression and Discriminant Analysis with sex as the protected attribute; and the precision is significantly degraded for other training algorithms and protected attributes like race."

Key Insights Distilled From

by Salvador Rob... at 05-01-2024
Predicting Fairness of ML Software Configuration

Deeper Inquiries

How can the proposed framework be extended to handle multiple protected attributes simultaneously?

The proposed framework can be extended to handle multiple protected attributes simultaneously by modifying the input data and model architecture. One approach is to encode the multiple protected attributes as separate features in the input data, allowing the model to learn the interactions between them. Additionally, the model architecture can be adjusted to accommodate multiple outputs corresponding to different protected attributes, enabling the prediction of fairness for each attribute independently. By incorporating these changes, the framework can effectively analyze the impact of hyperparameters on fairness across various protected attributes simultaneously.

What are the potential limitations of using regression models to predict fairness, and how can these limitations be addressed?

One potential limitation of using regression models to predict fairness is the assumption of linearity between hyperparameters and fairness metrics, which may not always hold true in complex real-world scenarios. To address this limitation, more sophisticated machine learning techniques such as ensemble methods or deep learning models can be explored to capture non-linear relationships effectively. Additionally, the choice of fairness metrics and the interpretability of the regression models can pose challenges in accurately predicting fairness. To mitigate these limitations, a comprehensive evaluation of different fairness metrics and model interpretability techniques can be conducted to ensure robust and reliable predictions of fairness.

How can the insights from this study be leveraged to develop automated tools for fairness-aware hyperparameter tuning in real-world software development workflows?

The insights from this study can be leveraged to develop automated tools for fairness-aware hyperparameter tuning by integrating the trained regression models into existing hyperparameter optimization frameworks. These tools can utilize the predictive models to recommend hyperparameter configurations that optimize both performance metrics and fairness simultaneously. By incorporating fairness considerations into the hyperparameter tuning process, developers can ensure that the resulting machine learning models are not only accurate but also fair and unbiased. Furthermore, the automated tools can provide real-time feedback on the fairness implications of different hyperparameter choices, enabling developers to make informed decisions during the model development process.