[Paper Review] Addressing Intersectionality, Explainability, and Ethics in AI-Driven Diagnostics: A Rebuttal and Call for Transdiciplinary Action

Myles Joshua Toledo Tan, Panayiotis V. Benos|ArXiv.org|Jan 15, 2025

Artificial Intelligence in Healthcare and Education7 citations

TL;DR

This paper rebuts a purely accuracy-focused view of AI diagnostics and calls for intersectionality, privacy, explainability, and transdisciplinary collaboration to ensure ethical, equitable AI-driven healthcare.

ABSTRACT

The increasing integration of artificial intelligence (AI) into medical diagnostics necessitates a critical examination of its ethical and practical implications. While the prioritization of diagnostic accuracy, as advocated by Sabuncu et al. (2025), is essential, this approach risks oversimplifying complex socio-ethical issues, including fairness, privacy, and intersectionality. This rebuttal emphasizes the dangers of reducing multifaceted health disparities to quantifiable metrics and advocates for a more transdisciplinary approach. By incorporating insights from social sciences, ethics, and public health, AI systems can address the compounded effects of intersecting identities and safeguard sensitive data. Additionally, explainability and interpretability must be central to AI design, fostering trust and accountability. This paper calls for a framework that balances accuracy with fairness, privacy, and inclusivity to ensure AI-driven diagnostics serve diverse populations equitably and ethically.

Motivation & Objective

• Highlight the ethical limitations of focusing solely on diagnostic accuracy in AI-driven medical diagnostics.
• Argue for incorporating intersectionality, social determinants of health, and broader determinants into AI systems.
• Emphasize privacy, data security, and governance to prevent harms from using sensitive attributes.
• Advocate for explainability, transparency, and participatory, transdisciplinary development.

Proposed method

• Critically analyze the stance of prioritizing accuracy over fairness in AI diagnostics.
• Propose a transdisciplinary framework integrating social sciences, ethics, and public health insights.
• Outline a framework with four pillars: Intersectional Fairness, Determinants of Health Integration, Privacy and Security, and Transdisciplinary Collaboration.
• Recommend methodological approaches for privacy-preserving AI (e.g., differential privacy, federated learning).
• Advocate for metrics and evaluation that reflect intersectionality and real-world determinants of health.

Descriptio 1: Framework for an Equity-Centered and AI-Driven Diagnostics. This framework illustrates the four pillars essential for achieving equitable and inclusive AI-driven diagnostics. These include Intersectional Fairness, Determinants of Health Integration (social; environmental; behavioral an

Experimental results

Research questions

• RQ1 How do intersectionality and social determinants of health influence the fairness of AI-driven diagnostics?
• RQ2 What framework and governance structures are needed to balance accuracy with fairness, privacy, and inclusivity in AI healthcare tools?
• RQ3 How can explainability and transdisciplinary collaboration be integrated into the AI development lifecycle to improve trust and equity?

Key findings

• Argues that reducing health disparities to discrete sub-populations and metrics risks perpetuating inequities.
• Emphasizes that race is a social construct without consistent biological basis and cautions against using race as a diagnostic proxy.
• Highlights privacy and data-security risks when using sensitive attributes to optimize AI performance.
• Advocates for incorporating lifestyle, environmental, and structural determinants of health into AI systems to reflect real-world complexities.
• Calls for transparency, interpretability, and governance to ensure accountability and trust in AI-driven diagnostics.
• Proposes a cohesive framework with four pillars to guide equitable AI diagnostics.