Masterclass: Equitable AI in Healthcare

A half-day discussion of machine learning in health care for researchers, PhD students,
equitable AI enthusiasts, data scientists and medical practitioners.

• Dr Chika Yinka-Banjo, Dept. of Computer Sciences, University of Lagos, Nigeria
• Dr Mary Akinyemi, Dept. of Mathematics & Statistics, Austin Peay State University, USA
• Dr Olasupo Ajayi, AI & Robotics Lab, University of Lagos, Nigeria
• David Tresner-Kirsch, Chief Technology Officer, Nivi, Inc, USA

This masterclass describes and evaluates a novel active learning approach for incrementally improving the accuracy of a Natural Language Processing (NLP), while optimising for gender-equitable outcomes in healthcare systems. The approach employs an iterative cyclic model, incorporating data annotation using NLP, human auditing to improve the annotation accuracy especially for data with demographic segmentation, testing on new data (with intentional bias favoring underperforming demographics), and a loopback system for retraining the model and applying it on new data. We describe experimental integration of the audit tool with distinct NLP tasks in two separate contexts:

1. annotation of medical symptoms collected in Hausa and English languages based on responses to a research questionnaire about health access in Northern Nigeria;
2. message intent classification in English and Swahili languages based on spontaneous user messages to a health guide chatbot in both Nigeria and Kenya.

Our findings indicate that this gender-aware audit workflow is language agnostic and capable of mitigating demographic inequity while improving overall system accuracy.

To register for in person and online, please visit the link: https://bit.ly/3SeFdu4

Please find the announcement of the Masterclass here: CORE-AI NITheCS_Masterclass _Zoom- 20240117