Overview
The Fair ML for Health Workshop is set to take place at the Neural Information Processing Systems (NeurIPS) 2019 conference. This workshop aims to explore the intersection of machine learning and healthcare, particularly focusing on fairness issues that arise in this domain. It presents a significant opportunity for researchers to contribute to discussions on how machine learning can address inequities in health systems.
Background & Relevance
The integration of machine learning in healthcare has the potential to revolutionize patient care and health outcomes. However, the deployment of these technologies raises critical questions regarding fairness and bias. As machine learning systems are increasingly utilized in clinical decision-making, it is essential to ensure that they do not perpetuate existing disparities. This workshop will serve as a platform for researchers to share insights and advancements in developing fair ML practices tailored for healthcare applications.
Key Details
- Event: Fair ML for Health Workshop
- Date: December 13-14, 2019
- Location: Vancouver, Canada
- Submission Deadline: September 9, 2019, 11:59 PM Anywhere on Earth (AoE)
- Submission Link: CMT Submission
- Contact Email: fairmlhealth.neurips19@gmail.com
- Workshop Website: fairmlforhealth.com
Eligibility & Participation
This workshop invites submissions from researchers, practitioners, and students who are working on fairness in machine learning within the healthcare sector. It is particularly relevant for those investigating how to mitigate bias and promote equitable health outcomes through machine learning technologies.
Submission or Application Guidelines
Interested participants are encouraged to submit extended abstracts ranging from 2 to 8 pages. Submissions that include publicly available data or code are highly encouraged, as transparency is crucial in advancing fair practices in ML for health.
Additional Context / Real-World Relevance
The focus on fairness in machine learning is increasingly vital as these technologies become more integrated into healthcare systems. Addressing issues of bias and inequity can lead to better health policies and resource allocation, ultimately improving access to care for underserved populations. This workshop aligns with ongoing efforts to ensure that machine learning applications contribute positively to public health.
Conclusion
The Fair ML for Health Workshop at NeurIPS 2019 represents a crucial gathering for those interested in the ethical implications of machine learning in healthcare. Researchers are encouraged to submit their work and engage in meaningful discussions that could shape the future of equitable health technology. Explore this opportunity to contribute to a vital area of research and practice in AI and healthcare.
Category: CFP & Deadlines
Tags: fairness, ml, healthcare, neuris, public-health, bias, clinical-decision-making, datasets
