Mitigating Algorithmic Bias in Healthcare Predictive Models through Fair Feature Selection

Core Concepts

A generalized fair feature selection framework that addresses both distributive and procedural fairness can improve the equity of healthcare predictive models while maintaining overall performance.

Abstract

The authors introduce a fair feature selection approach to mitigate algorithmic biases in machine learning for healthcare applications. They evaluate their method on three distinct healthcare datasets: Tappy Keystroke (for Parkinson's detection), Clinical and Molecular Features data (for Glioma Grading), and Hospital Admission Data (for Coronary Artery Disease).
The key highlights are:

The fair feature selection process involves partitioning the datasets by sensitive attributes (gender) and applying multiple feature ranking techniques separately on each partition. This helps identify features that are equally important across demographic groups.

The final feature selection is based on a combined metric that balances fairness (measured by Disparate Impact, Statistical Parity, and Equalized Odds) and overall prediction accuracy (Balanced Accuracy).

On all three datasets, the fairness-oriented feature selection approach led to notable improvements in fairness metrics while maintaining a minimal degradation in balanced accuracy, compared to the standard feature selection methods.

The authors demonstrate that integrating multiple feature selection techniques and considering both distributive and procedural fairness can effectively mitigate biases in healthcare predictive models.

Limitations include the focus on only gender biases, high computational requirements, and reliance on data quality. Future work should expand the evaluation to other demographic factors and enhance the efficiency and fairness-awareness of the feature selection approach.

Stats

The Tappy Keystroke dataset has 31 features and 83 data points.
The Clinical and Molecular Features data for Glioma Grading has 22 features and 839 data points.
The Hospital Admission Data for Coronary Artery Disease has 53 features and 6611 data points.

Quotes

"Our work indicates that a generalized fair feature selection framework, considering both distributive and procedural fairness, is achievable."
"The results from our analysis show that combining multiple feature selection methods outperforms single method approaches in terms of fairness and accuracy."
"This work contributes to the expanding field of ethical artificial intelligence in healthcare by presenting a method to ensure equity in predictive modeling—a critical consideration in healthcare settings where decision-making profoundly affects outcomes."

Key Insights Distilled From

Evaluating Fair Feature Selection in Machine Learning for Healthcare

by Md Rahat Sha... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19165.pdf

Deeper Inquiries

How can the proposed fair feature selection approach be extended to handle a broader spectrum of demographic attributes beyond gender?

The fair feature selection approach proposed in the study can be extended to handle a broader spectrum of demographic attributes by incorporating a more comprehensive set of sensitive attributes. This extension would involve identifying and stratifying the datasets based on multiple demographic factors such as race, age, socioeconomic status, and geographical location. By partitioning the data according to these additional attributes, the feature selection process can be tailored to address biases that may arise from various demographic groups.
To handle a broader spectrum of demographic attributes, the feature ranking techniques used in the study can be adapted to consider the unique characteristics and interactions of each demographic subgroup. This adaptation may involve developing new feature selection methods that are specifically designed to capture the nuances of different demographic attributes. Additionally, the aggregation of feature rankings across multiple demographic groups can provide a more holistic view of feature importance and fairness considerations.
Furthermore, the fair feature selection framework can be enhanced to incorporate fairness-aware metrics that are tailored to specific demographic attributes. By integrating fairness metrics that are relevant to each demographic group, the feature selection process can prioritize features that contribute to equitable outcomes across diverse populations. This extension would require a nuanced understanding of the impact of different demographic attributes on model performance and fairness.

What algorithmic and computational enhancements could be made to the feature selection framework to improve its efficiency and scalability?

To improve the efficiency and scalability of the fair feature selection framework, several algorithmic and computational enhancements can be implemented:

Parallel Processing: Utilizing parallel processing techniques can help expedite the feature selection process by distributing computations across multiple cores or nodes. This can significantly reduce the time required for feature ranking and selection, especially when dealing with large datasets.

Optimization Algorithms: Implementing optimization algorithms such as genetic algorithms or simulated annealing can enhance the search for optimal feature subsets. These algorithms can efficiently explore the feature space and identify the most relevant features while considering fairness constraints.

Feature Importance Sampling: Instead of evaluating all features in each iteration, feature importance sampling techniques can be employed to prioritize the evaluation of features that are more likely to contribute to fairness and accuracy. This can streamline the feature selection process and improve its efficiency.

Incremental Learning: Incorporating incremental learning techniques can allow the feature selection framework to adapt to new data and evolving fairness requirements over time. This adaptive approach can enhance the scalability of the framework and ensure its relevance in dynamic healthcare settings.

Model Compression: Implementing model compression techniques can reduce the computational complexity of the predictive models trained on the selected features. By compressing the models without sacrificing performance, the overall efficiency of the feature selection framework can be improved.

What are the potential implications of this fair feature selection technique on the interpretability and explainability of the resulting healthcare predictive models?

The fair feature selection technique proposed in the study can have significant implications for the interpretability and explainability of the resulting healthcare predictive models:

Enhanced Transparency: By prioritizing features that contribute to fairness across demographic groups, the selected features are more likely to align with ethical considerations and societal norms. This alignment enhances the transparency of the predictive models and makes their decision-making process more interpretable.

Reduced Bias in Interpretations: The fair feature selection approach aims to mitigate biases in the model by ensuring that features are selected based on their relevance and fairness considerations. This reduction in bias can lead to more accurate and unbiased interpretations of the model's predictions and decisions.

Improved Model Explanations: The selected features are more likely to be associated with meaningful and non-discriminatory factors, making it easier to explain the model's predictions to stakeholders, including healthcare providers and patients. The feature selection process contributes to the overall explainability of the predictive models.

Compliance with Regulatory Requirements: Healthcare predictive models developed using fair feature selection techniques are more likely to comply with regulatory requirements related to fairness and transparency. This compliance enhances the trustworthiness of the models and facilitates their adoption in clinical settings.

Facilitated Stakeholder Engagement: The emphasis on fairness in feature selection can promote stakeholder engagement by providing insights into how demographic attributes influence the model's predictions. This engagement can lead to more informed decision-making and foster collaboration between data scientists, healthcare professionals, and policymakers.