Paper

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Qin, Hong, Wang, Xiaoqian, Effoduh, Jake Okechukwu, Muyingo, Sylvia Kiwuwa, Engin, Zeynep, Hwa, Rebecca, Seyyed-Kalantari, Laleh, Moore, Candace Makeda, Zhang, Yiye, Ahluwalia, Ramneek, Parikh, Ravi, Kong, Jude, Ding, Wandi, Schwartz, Reva, Guo, Serena Jingchuan, Zhu, Dongxiao, Christo El Morr

2023

Overview

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.

• Publisher: Cornell University Library, arXiv.org
• Subject: AI ethics / Artificial intelligence / Bias / Data transparency / Risk management / Trustworthiness