Presentation
Addressing Bias in AI/ML: Ensuring Fairness and Equity in Neurocritical Care
DescriptionBias in Artificial Intelligence and Machine Learning (AI/ML) algorithms presents a significant challenge in neurocritical care, potentially leading to inequitable patient outcomes and treatment disparities. This session will delve into the complex issue of bias in AI/ML models deployed in neurocritical care settings. Attendees will explore the various sources of bias, including data collection practices, algorithm design, and societal factors, and their impact on decision-making processes. Through case studies and real-world examples, participants will gain insights into how bias manifests in AI/ML applications, such as differential diagnostic accuracy across demographic groups or unequal access to healthcare resources. Moreover, the session will discuss strategies for detecting, mitigating, and preventing bias in AI/ML models, ranging from algorithmic fairness techniques to diverse and representative dataset curation. By fostering a deeper understanding of bias in AI/ML, this session aims to empower attendees to champion fairness and equity in neurocritical care through responsible AI implementation.
Event Type
Breakout Session
TimeThursday, October 17th9:55am - 10:15am PDT
LocationHarbor Ballroom A
Delivery, Quality and Safety
APP Practice
Diversity, Equity, and Inclusion
Global Neurocritical Care
Informatics
Patient Education
Provider Education Topics (eg fellowship training, competency assessment, etc)
Introductory