From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health
arXiv:2606.11214v1 Announce Type: cross Abstract: Algorithmic fairness is essential for responsible ML-driven public health research, yet its practical implementation remains limited. To investigate this awareness-action gap, we conducted a sequential mixed-methods study comprising expert interviews, an online survey, and systematic mapping. The expert interviews informed the design of the survey, which in turn revealed fragmented definitions of fairness, limited training and guidance, reliance on external sources, and rare use of formal assessment, mitigation, or monitoring. These findings we
The proliferation of AI in sensitive domains like public health brings the ethical implementation of algorithmic fairness to the forefront, making this research timely and critical.
A strategic reader should care because the inability to implement algorithmic fairness effectively in public health can erode trust in AI, lead to inequitable outcomes, and hinder the adoption of beneficial AI systems.
This research provides concrete insights into the barriers preventing practical algorithmic fairness, shifting the focus from theoretical awareness to identifying actionable steps for improvement.
- · AI ethics researchers
- · Public health organizations
- · Patients in underserved communities
- · Developers ignoring fairness principles
- · Public health systems with biased AI
- · Companies offering 'black box' AI solutions
Increased funding and research into practical algorithmic fairness tools and methodologies.
Development of industry standards and regulatory frameworks specifically for AI fairness in public health applications.
Improved health equity outcomes as AI systems are designed and deployed with a greater emphasis on mitigating biases.
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Read at arXiv cs.AI