Quantifying and Bounding Fairness and Unfairness in Binary and Multiclass Classification

核心概念

This work proposes a new interpretable measure of unfairness that allows providing a quantitative analysis of classifier fairness, beyond a dichotomous fair/unfair distinction. It generalizes the equalized odds fairness criterion to the multiclass setting and provides methods for auditing classifiers for fairness when the confusion matrices are not available.

摘要

The key highlights and insights of this content are:

1. The authors propose a new interpretable measure of unfairness that quantifies the fraction of the population treated differently from an underlying baseline treatment. This measure has a clear probabilistic interpretation and satisfies desirable properties.

2. The authors generalize the equalized odds fairness criterion, originally defined for binary classification, to the multiclass setting. The multiclass equalized odds criterion requires that the confusion matrices of the classifier be the same across all sub-populations.

3. When the confusion matrices of the classifier in each sub-population are known, the authors show how to calculate the unfairness measure exactly for binary classification, and provide algorithms to obtain upper and lower bounds for the multiclass case.

4. In the more challenging case where only label proportions are available, without access to the confusion matrices, the authors propose methods to lower-bound the unfairness and the error of the classifier. This is done by exploiting the inherent trade-off between accuracy and fairness.

5. The authors report experiments demonstrating the effectiveness of their proposed methods in various scenarios, including applications such as election prediction, disease diagnosis, and analysis of access to healthcare.

統計資料

None.

引述

None.

從以下內容提煉的關鍵洞見

Fairness and Unfairness in Binary and Multiclass Classification

by Sivan Sabato... 於 arxiv.org 04-09-2024

https://arxiv.org/pdf/2206.03234.pdf

深入探究

How can the proposed unfairness measure be extended to other fairness notions beyond equalized odds, such as demographic parity or equal opportunity

The proposed unfairness measure can be extended to other fairness notions beyond equalized odds by adapting the definition of unfairness to align with the specific requirements of the alternative fairness criteria. For instance, in the case of demographic parity, which focuses on ensuring that the distribution of predicted outcomes is equal across different demographic groups, the unfairness measure could be modified to capture disparities in these distributions. By adjusting the calculation of unfairness to consider the demographic composition of the population and the predicted outcomes, it can be tailored to assess compliance with demographic parity. Similarly, for equal opportunity, which aims to guarantee equal true positive rates across different groups, the unfairness measure could be redefined to evaluate the extent to which these rates vary between groups. By incorporating the key principles of each fairness notion into the calculation of unfairness, the measure can be effectively applied to assess the performance of classifiers in meeting these specific criteria.

What are the implications of the accuracy-fairness trade-off on the design and deployment of machine learning systems in high-stakes applications

The accuracy-fairness trade-off has significant implications for the design and deployment of machine learning systems in high-stakes applications. In scenarios where accuracy is prioritized over fairness, there is a risk of perpetuating biases and discrimination against certain groups within the population. This can lead to unfair outcomes and reinforce existing disparities, particularly in critical areas such as healthcare, finance, and criminal justice. On the other hand, prioritizing fairness over accuracy may result in reduced predictive performance and potentially compromise the overall effectiveness of the system. Balancing these competing objectives requires careful consideration of the trade-offs involved and the ethical implications of the decisions made. It underscores the importance of developing algorithms that can achieve a balance between accuracy and fairness, ensuring that the outcomes generated by machine learning models are both reliable and equitable.

How can the insights from this work be leveraged to develop new techniques for learning fair classifiers when only limited aggregate statistics are available about the target population

The insights from this work can be leveraged to develop new techniques for learning fair classifiers when only limited aggregate statistics are available about the target population. By utilizing the proposed unfairness measure and the methodology outlined in the study, researchers and practitioners can design algorithms that prioritize fairness while operating under constraints of limited data access. This approach involves incorporating the principles of fairness into the learning process, even in the absence of detailed individual-level information. By focusing on aggregate statistics and deriving insights on classifier fairness from this data, it is possible to develop robust and equitable machine learning models that align with ethical standards and regulatory requirements. Additionally, the techniques presented in the study can be extended to various real-world applications where individual-level data is scarce, enabling the development of fair and transparent AI systems in diverse domains.