Learning Social Preferences for Fairness in Kidney Placement Algorithms

Core Concepts

Non-expert stakeholders' preferences for group fairness notions can be effectively learned to assess the fairness of machine learning-based kidney placement algorithms.

Abstract

This paper investigates the public perception of fairness of a machine learning-based kidney acceptance rate predictor (ARP) used in the kidney placement pipeline. A human-subject survey experiment was conducted on the Prolific crowdsourcing platform to collect feedback from 75 non-expert participants regarding the fairness of the ARP.
The key highlights and insights from the study are:

A novel logit-based feedback model is proposed to capture the ambiguity in participants' preferences across diverse group fairness notions.

A projected gradient-descent algorithm with efficient gradient computation is designed to learn the social preference weights that minimize the feedback regret.

The proposed approach is validated through simulation experiments as well as the analysis of the Prolific survey dataset.

The public preferences indicate that accuracy equality and predictive equality are the most preferred group fairness notions in the context of kidney placement. The ARP is perceived as a reasonably fair system by the non-expert participants.

The study provides valuable insights into the public's understanding and expectations of fairness in the context of kidney placement algorithms, which can inform the development of fair and socially acceptable AI systems.

Stats

The kidney matching dataset (STAR file) requested from the Organ Procurement and Transplant Network (OPTN) was used to generate the data tuples presented to the survey participants.

Quotes

"Accuracy equality and predictive equality can be deemed as critical group fairness notions from the public stakeholders' viewpoint."
"As a follow-up to the above claim, it is also natural to conclude that the non-expert participants' perceive ARP as a reasonably fair system when deployed in the kidney placement pipeline."

Key Insights Distilled From

Learning Social Fairness Preferences from Non-Expert Stakeholder Opinions in Kidney Placement

by Mukund Teluk... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03800.pdf

Deeper Inquiries

How can the proposed fairness feedback model and learning algorithm be extended to incorporate expert stakeholders' (e.g., transplant surgeons) preferences in addition to the public's views

To incorporate expert stakeholders' preferences alongside the public's views in the fairness feedback model and learning algorithm, a hybrid approach can be adopted. This approach would involve collecting feedback from both non-expert stakeholders (public participants) and expert stakeholders (transplant surgeons) and weighting their preferences accordingly.

Data Collection: Gather feedback from transplant surgeons on the fairness of the ML-based system in kidney placement. This feedback can be obtained through surveys, interviews, or focus groups.

Preference Integration: Develop a mechanism to combine the preferences of both non-expert and expert stakeholders. This could involve assigning different weights to each group's feedback based on their expertise and relevance to the decision-making process.

Algorithm Modification: Modify the fairness feedback model to accommodate the preferences of both groups. This may involve adjusting the aggregation of fairness evaluations to consider the input from both non-expert and expert stakeholders.

Learning Algorithm Extension: Extend the learning algorithm to optimize the social preference weights based on the combined feedback from both groups. This would involve updating the algorithm to minimize the overall feedback regret considering the preferences of all stakeholders.

By integrating the preferences of expert stakeholders with those of non-expert stakeholders, the fairness feedback model and learning algorithm can provide a more comprehensive and balanced evaluation of the ML-based system in kidney placement.

What are the potential challenges and limitations in deploying the learned fairness preferences in the real-world kidney placement pipeline, and how can they be addressed

Deploying learned fairness preferences in real-world kidney placement pipelines may face several challenges and limitations:

Bias in Data: The historical data used to train the ML models may contain inherent biases that could perpetuate unfair outcomes. Address this by continuously monitoring and updating the training data to ensure fairness.

Interpretability: ML models used in kidney placement may be complex and lack interpretability, making it challenging to understand how fairness preferences are being incorporated. Develop explainable AI techniques to enhance transparency.

Regulatory Compliance: Adhering to regulatory requirements and ethical guidelines in healthcare settings is crucial. Ensure that the deployment of fairness preferences complies with regulations such as HIPAA and GDPR.

Algorithmic Fairness: Ensure that the learned fairness preferences do not inadvertently introduce new biases or unfairness into the decision-making process. Regular audits and bias assessments can help mitigate this risk.

Addressing these challenges involves a multi-faceted approach that includes data governance, model transparency, regulatory compliance, and ongoing monitoring of algorithmic fairness.

Given the importance of fairness in healthcare applications, how can the insights from this study be applied to improve fairness in other medical decision-making systems beyond kidney placement

The insights from this study on fairness in kidney placement can be applied to improve fairness in other medical decision-making systems beyond kidney placement:

Organ Allocation: Apply similar fairness feedback models to organ allocation systems for other organs like the heart, liver, and lungs to ensure equitable distribution based on medical need and other relevant factors.

Clinical Trials: Use the principles of fairness feedback to evaluate the selection criteria and participant recruitment processes in clinical trials, ensuring diverse representation and equitable access to experimental treatments.

Disease Diagnosis: Implement fairness considerations in diagnostic algorithms to prevent biases based on demographic factors and ensure accurate and unbiased disease diagnosis and treatment recommendations.

Treatment Recommendations: Incorporate fairness feedback mechanisms in treatment recommendation systems to account for individual patient characteristics and preferences, promoting personalized and equitable healthcare delivery.

By applying the lessons learned from kidney placement to other medical decision-making systems, healthcare organizations can enhance fairness, transparency, and equity in patient care and outcomes.