Seminar Series: Melissa McCradden, M.HSc, PhD
Bias at the Bedside? Addressing Algorithmic Bias for Promotion of Equitable Healthcare
Melissa McCradden, M.HSc, PhD
Bioethicist
Department of Bioethicsddd
The Hospital for Sick Children (SickKids), Toronto, ON
Researcher
Genetics & Genome Biology Program
The Hospital for Sick Children (SickKids), Toronto, ON
Assistant Professor
University of Toronto
Abstract/Keywords:
Algorithmic bias poses a major threat to the equitable and fair translation of healthcare artificial intelligence systems. While there is a great deal of work documenting disparities in prediction models for various medical tasks, relatively lesser work tackles what to do in the face of these disparities. In this talk, we will (1) discuss the problem of algorithmic bias in healthcare AI work; (2) identify how bias is implicated in model development, validation, and deployment; (3) identify the ethical implications of algorithmic bias; (4) discuss algorithmic fairness methodologies; (5) utilize an ethical analysis to explore strategies to address bias through the lens of distributive justice and equity. Taking an ethics lens to the bias problem throughout the AI pipeline can enhance ethical awareness among data scientists and enable robust empirical characterization of model performance to inform fairness considerations for deployment.
Short Biography:
Dr. McCradden is a Bioethicist with the Department of Bioethics and Researcher with the Genetics & Genome Biology Program at The Hospital for Sick Children. Melissa holds a PhD in Neuroscience (McMaster University) and a M.HSc. in Bioethics (University of Toronto). She is an Assistant Professor at the University of Toronto. In her role, she provides clinical and organizational consultation, gives education to staff and trainees, participates and leads policy development, and conducts research. Her areas of scholarship and research include artificial intelligence/machine learning, precision child health, paediatric bioethics, and research ethics. She is a member of the AI in Medicine Steering Committee at SickKids and sits on multiple international consensus groups pertaining to robust clinical evaluation of medical AI systems.
