Equitable AI in Health Care
- Date: 17 January 2024, 11:00–14:30
- Location: Online event, Ångström Laboratory, Ångström laboratory
- Type: Workshop
- Lecturer: Dr Chika Yinka-Banjo Dept. of Computer Sciences University of Lagos, Nigeria Dr Mary Akinyemi Dept. of Mathematics & Statistics Austin Peay State University, USA
- Web page
- Organiser: Kajsa Hallberg Adu
- Contact person: Kajsa Hallberg Adu
- Phone: +46700636835
A half-day discussion of machine learning in health care for researchers, PhD students, equitable AI enthusiasts, data scientists and medical practitioners. Registrera dig här: https://nithecs-ac-za.zoom.us/webinar/register/WN_lj2xK1RiTpm3PIA72QjdwA#/registration
The Africa-Europe Cluster of Research Excellence: Addressing Global and African Challenges Through Methods of Artificial Intelligence, Data Science, and Theoretical and Computational Thinking
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:i. annotation of medical symptoms collected in Hausa and English languages based on responses to a research questionnaire about health access in Northern Nigeria; ii. 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.