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machine learning

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3

RESEARCH · 1 SOURCE · arXiv cs.AI

Preprint: Algorithmic Bias from the Perspectives of Healthcare Professionals

This arXiv preprint reports survey and interview data from healthcare professionals (clinical and administrative roles) about algorithmic bias in ML/AI-driven healthcare information systems. The study finds that views on causes of bias, assessment criteria, perceived fairness, likely acceptance, and mitigation approaches vary by job role, task, and algorithm characteristics, and calls for further research to ensure fairness in healthcare.

5.0

RESEARCH · 1 SOURCE · The Verge AI

TACLS: Scripps’ new satellite + machine-learning tool to help detect flash floods

Researchers at the Scripps Institution of Oceanography (UC San Diego) developed the Transient Artifact and Continuous Learning System (TACLS), software that combines satellite data and machine learning to help National Weather Service forecast offices spot areas at risk of flash flooding sooner and support warning decisions. The project is presented as a new decision-support tool intended to improve lead time and accuracy of flash-flood alerts rather than a completed, nationwide operational system.

7.0