Assessing and Improving Procedural Fairness in Machine Learning Models

Core Concepts

Procedural fairness in machine learning models refers to the fairness of the decision-making process, rather than just the fairness of the outcomes. This paper proposes a novel metric to evaluate the group procedural fairness of ML models based on feature attribution explanation (FAE), and develops methods to identify and mitigate sources of procedural unfairness.

Abstract

The paper first defines procedural fairness in machine learning, distinguishing it from distributive fairness which focuses on fairness of outcomes. It then proposes a novel metric called GPFFAE to quantify group procedural fairness, which uses FAE techniques to capture the decision process of the ML model.
The key highlights and insights are:

Procedural fairness is defined as the fairness of the internal decision logic/process of the ML model, without prejudice or preference for individuals or groups. This is distinct from distributive fairness which focuses on fairness of outcomes.

The GPFFAE metric is proposed to assess group procedural fairness. It measures the distributional difference in FAE explanations between similar data points from different groups.

Experiments on synthetic and real-world datasets show GPFFAE can accurately distinguish procedural-fair and procedural-unfair models. It also reveals the complex relationship between procedural and distributive fairness.

A method is developed to identify the features that lead to procedural unfairness by detecting significant differences in FAE explanations between groups.

Two mitigation methods are proposed to improve procedural fairness by addressing the identified unfair features, which also improve distributive fairness.

The summary provides a comprehensive understanding of the paper's key contributions in defining, measuring, and enhancing procedural fairness in machine learning.

Stats

"As artificial intelligence (AI) is increasingly being used in critical domains (like finance [1], hiring [2], and criminal justice [3]) to make consequential decisions affecting individuals, concerns about discrimination and fairness inevitably arise and have become the forefront of deliberations within the realm of AI ethics [4]."
"Until now, relatively little attention has been paid to procedural fairness [5], [7], [8], [15], and even a clear definition of procedural fairness in ML models is lacking [16]."

Quotes

"Procedural fairness emphasizes fairness in the decision-making process [7], [8]."
"People are more willing to support an unfair outcome when they feel the process is fair [7], [13]."
"The urgency of research around procedural fairness (including its conceptual definition, evaluation methods, and related aspects) is clear. However, within the overall field of AI fairness, the study of procedural fairness is currently in its infancy, highlighting the need for more and enhanced research efforts."

Key Insights Distilled From

Procedural Fairness in Machine Learning

by Ziming Wang,... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01877.pdf

Deeper Inquiries

How can the proposed procedural fairness metric GPFFAE be extended to handle datasets with sparse or imbalanced sample distributions

To handle datasets with sparse or imbalanced sample distributions, the proposed procedural fairness metric GPFFAE can be extended in several ways:

Sampling Techniques: Utilize sampling techniques such as oversampling, undersampling, or synthetic data generation to balance the dataset. This can help in creating more similar pairs of samples for the GPFFAE metric evaluation.

Feature Engineering: Conduct feature engineering to create new features that can help in better distinguishing between similar samples. This can aid in improving the similarity between pairs of samples in sparse datasets.

Weighted GPFFAE: Introduce weighting mechanisms in the GPFFAE metric to give more importance to the explanations of samples from the minority class or underrepresented group. This can help in addressing the imbalance in sample distributions.

Ensemble Methods: Employ ensemble methods to combine multiple GPFFAE evaluations on different subsets of the dataset. This can provide a more robust assessment of procedural fairness in the presence of sparse or imbalanced data distributions.

What are the potential limitations of using FAE techniques to capture the decision process, and how can alternative XAI methods be leveraged to assess procedural fairness

Using FAE techniques to capture the decision process may have limitations such as:

Interpretability: FAE methods may not always provide a clear and interpretable explanation of the model's decision process, especially in complex models like deep neural networks.

Bias in Explanations: FAE methods may introduce bias in the explanations, leading to incorrect assessments of procedural fairness.

Limited Scope: FAE techniques may not capture all aspects of the decision process, potentially missing important factors influencing fairness.

To address these limitations, alternative XAI methods can be leveraged to assess procedural fairness:

Counterfactual Explanations: Utilize counterfactual explanations to understand how changes in input features affect the model's decisions, providing insights into the decision-making process.

Local Interpretable Model-agnostic Explanations (LIME): LIME can be used to generate local explanations for individual predictions, offering a more granular understanding of the model's behavior.

SHAP Values: SHAP (SHapley Additive exPlanations) values can provide a more comprehensive and theoretically grounded approach to feature attribution, offering a deeper insight into the model's decision process.

Model-specific Explanations: Develop model-specific explanation techniques tailored to the characteristics of the ML model being evaluated, ensuring a more accurate assessment of procedural fairness.

Given the complex relationship between procedural and distributive fairness, how can ML models be designed to optimally balance these two dimensions of fairness

To optimally balance procedural and distributive fairness in ML models, the following strategies can be employed:

Fairness Constraints: Incorporate fairness constraints during model training to ensure that both procedural and distributive fairness are considered as optimization objectives. This can be achieved through regularization techniques or fairness-aware loss functions.

Fairness-aware Feature Selection: Implement feature selection methods that prioritize features contributing to both procedural and distributive fairness. This can help in designing models that are fair at both levels.

Adaptive Fairness Trade-offs: Develop algorithms that dynamically adjust the trade-off between procedural and distributive fairness based on the specific requirements of the application domain. This adaptive approach can ensure a flexible and context-aware fairness framework.

Feedback Mechanisms: Implement feedback mechanisms that continuously monitor and evaluate the fairness performance of the model in real-world scenarios. This feedback can be used to iteratively improve the balance between procedural and distributive fairness.

By integrating these strategies, ML models can be designed to strike an optimal balance between procedural and distributive fairness, ensuring ethical and unbiased decision-making processes.