Auditing Fairness under Unobserved Confounding: Framework and Real-world Application

Conceptos Básicos

Auditing inequity in resource allocation under unobserved confounding is possible with informative bounds, as demonstrated in a real-world study.

Resumen

The content discusses the challenges of quantifying inequity in decision-making systems due to unobserved confounding. It introduces a framework to audit fairness in resource allocation, providing bounds on treatment rates among high-risk individuals. The real-world application focuses on Paxlovid allocation for COVID-19 patients, revealing racial inequities that cannot be explained by unobserved factors. The study also includes semi-synthetic and synthetic tasks to validate the approach's effectiveness.

Introduction

Inequity across demographic lines poses challenges.
Need for accurate measurements of individual risk.

Identification under Strong Assumptions

Assumption of no unmeasured confounding.
Consistency and Covariate Stability assumptions.

Partial Identification under Arbitrary Unmeasured Confounding

Derivation of bounds on treatment rate among the needy.
Incorporation of covariate information for tighter bounds.

Sensitivity Analysis under Bounded Confounding

Introduction of sensitivity parameter γ.
Bounds with γ for assessing disparities across subgroups.

Benchmarking Sensitivity Analysis

Benchmarking analysis using diabetes as a covariate.

Results

Application to real-world study on Paxlovid allocation.
Semi-synthetic and synthetic settings validation.

Discussion

Introduction of a machine learning-based approach for auditing fairness.
Demonstration of robust quantification of need-based inequity.

Estadísticas

Best Case (Upper Bound) = 3/3
Worst Case (Lower Bound) = 0/3
Treatment Rate = 0.4
Mortality Rate = 0.3

Citas

"In this paper, we show that one can still give informative bounds on allocation rates among high-risk individuals."
"We demonstrate the effectiveness of our framework on a real-world study of Paxlovid allocation to COVID-19 patients."

Ideas clave extraídas de

Auditing Fairness under Unobserved Confounding

by Yewon Byun,D... a las arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14713.pdf

Consultas más profundas

How can the framework be adapted for other domains beyond healthcare?

The framework presented in the context of auditing fairness under unobserved confounding in healthcare settings can be adapted and applied to various other domains beyond healthcare. One key aspect is the focus on inequity across demographic lines, which is a pervasive issue in many decision-making systems. By modifying the covariates and outcomes of interest, this framework can be extended to areas such as housing assistance, lending practices, criminal justice systems, employment opportunities, educational access, and more. The fundamental principles of identifying inequities based on need-based allocation rates among high-risk individuals remain applicable across different domains.

What are the implications of relying on observed covariates for fairness auditing?

Relying solely on observed covariates for fairness auditing may introduce biases and limitations into the analysis. While observed covariates provide valuable information about individuals' characteristics that influence decision-making processes, they may not capture all relevant factors contributing to inequities. This reliance could lead to incomplete or inaccurate assessments of fairness if important confounders are omitted or unaccounted for in the analysis.
Furthermore, using only observed covariates may overlook systemic biases embedded within historical data collection practices or societal norms. These biases could perpetuate existing disparities and hinder efforts to achieve true equity in resource allocation or decision-making processes.
To address these implications effectively, it is crucial to combine insights from both observed and unobserved factors while conducting fairness audits. Incorporating methods like sensitivity analyses or partial identification techniques can help mitigate some of these limitations by providing bounds that account for potential unobserved confounding variables.

How might societal biases impact the interpretation of results from this framework?

Societal biases play a significant role in shaping how results from this framework are interpreted and understood. These biases can manifest at various stages throughout the analysis process:

Data Collection: Biases present in historical data collection practices may lead to skewed representations of certain demographic groups or outcomes. This bias could influence model training and subsequent interpretations derived from these models.

Model Development: If machine learning models used within the framework are trained on biased datasets (reflecting societal prejudices), they will inherently perpetuate those biases during predictions and estimations.

Interpretation: Societal norms and preconceptions about specific groups could affect how results are perceived or acted upon post-analysis. Interpretations might inadvertently reinforce existing stereotypes or discriminatory practices if not critically examined through an unbiased lens.

4..Decision-Making: The recommendations generated by this framework could potentially exacerbate inequalities if implemented without considering broader social contexts or ethical considerations related to equity.
Addressing societal biases requires a comprehensive approach that involves diversity-aware data collection strategies, algorithmic transparency measures, ongoing bias monitoring during model development phases,
and critical reflection on how findings align with broader goals
of promoting fairness and justice across diverse populations