Year: 2023
Type: preprint
Abstract: Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustworthy AI in global health, we convened a workshop on Fairness in Machine Intelligence for Global Health (FairMI4GH). The event brought together a global mix of experts from various disciplines, community health practitioners, policymakers, and more. Topics covered included managing AI bias in socio-technical systems, AI's potential impacts on global health, and balancing data privacy with transparency. Panel discussions examined the cultural, political, and ethical dimensions of AI in global health. FairMI4GH aimed to stimulate dialogue, facilitate knowledge transfer, and spark innovative solutions. Drawing from NIST's AI Risk Management Framework, it provided suggestions for handling AI risks and biases. The need to mitigate data biases from the research design stage, adopt a human-centered approach, and advocate for AI transparency was recognized. Challenges such as updating legal frameworks, managing cross-border data sharing, and motivating developers to reduce bias were acknowledged. The event emphasized the necessity of diverse viewpoints and multi-dimensional dialogue for creating a fair and ethical AI framework for equitable global health.
Source: arXiv (Cornell University)
Authors: Qin, Hong, Kong, Jude, Ding, Wandi, Ahluwalia, Ramneek, Morr, Christo El, Engin, Zeynep, Effoduh, Jake Okechukwu, Hwa, Rebecca, Guo, Serena Jingchuan, Seyyed-Kalantari, Laleh, Muyingo, Sylvia Kiwuwa, Moore, Candace Makeda, Parikh, Ravi, Schwartz, Reva, Zhu, Dongxiao, Wang, Xiaoqian, Zhang, Yiye
Institutions: University of Tennessee at Chattanooga, York University, Middle Tennessee State University, University of Pittsburgh, University of Florida, African Population and Health Research Center, Netherlands eScience Center, University of Pennsylvania, National Institute of Standards and Technology, Wayne State University, Purdue University West Lafayette, Weill Cornell Medicine
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4386647800