Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare

Core Concepts

The author introduces the Fairness-Aware Interpretable Modeling (FAIM) framework to balance model performance and fairness in healthcare machine learning.

Abstract

The study addresses concerns about model fairness and interpretability in healthcare machine learning. FAIM aims to improve fairness without compromising model performance by prioritizing fairer models among nearly-optimal ones. The framework integrates clinical expertise, multiple fairness metrics, and SHapley Additive exPlanations (SHAP) for enhanced interpretability. FAIM significantly mitigated biases related to sex and race in predicting hospital admission using real-world databases, outperforming common bias-mitigation methods.

Stats

FAIM significantly mitigated biases measured by fairness metrics by 53.5%-57.6% for the MIMIC-IV-ED case and 17.7%-21.7% for the SGH-ED case.
FAIM maintained comparable performance to baseline models with AUC values of 0.786 for MIMIC-IV-ED and 0.802 for SGH-ED.
Sensitivity and specificity values achieved by FAIM were on par with baseline models.

Quotes

"Fairness can be enhanced without sacrificing model performance."
"FAIM promotes active clinician engagement and fosters multi-disciplinary collaboration."

Key Insights Distilled From

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare

by Mingxuan Liu... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05235.pdf

Deeper Inquiries

How can the FAIM framework be adapted to other high-stakes domains beyond healthcare?

The FAIM framework's adaptability to other high-stakes domains beyond healthcare lies in its core principles of fairness-awareness and interpretability. By prioritizing fairness while maintaining model performance, FAIM can be applied to fields like finance, criminal justice, or even social services where biased decision-making can have significant consequences. The key adaptation would involve customizing the sensitive variables and fairness metrics based on the specific domain requirements. Additionally, integrating domain experts into the model selection process ensures that contextualized fairness considerations are incorporated effectively.

What are potential limitations or criticisms of the approach taken by FAIM?

While FAIM offers a promising solution for balancing model performance and fairness, there are potential limitations and criticisms associated with this approach. One criticism could be around the complexity of defining and measuring fairness metrics accurately across diverse populations or contexts. There might also be challenges in ensuring that sensitive variables are appropriately identified and handled within different datasets or applications. Moreover, some critics may argue that achieving true algorithmic fairness is an ongoing challenge due to inherent biases present in data collection processes.

How might the concept of fairness in AI intersect with broader societal issues beyond healthcare?

Fairness in AI extends far beyond healthcare and intersects with broader societal issues related to equity, diversity, inclusion, and social justice. In areas such as hiring practices, loan approvals, criminal justice systems, or education access, biased algorithms can perpetuate existing inequalities and reinforce systemic discrimination. Addressing these societal issues through fair AI involves not only technical solutions but also policy changes, ethical considerations, transparency measures, and community engagement efforts. Fairness in AI has implications for human rights protection laws as well as shaping public perceptions about trustworthiness in automated decision-making systems across various sectors.