Humanising Healthcare podcast - "AI Bias and Women"

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Humanising Healthcare - AI, Bias, and Women

In this edition:

Manasi is joined by PhD candidates Mackenzie Jorgensen and Tiarna Lee to discuss Artificial Intelligence (AI), algorithmic fairness and the need for bias mitigation methods in datasets used for AI.

Tiarna Lee is a PhD candidate at the School of Biomedical Engineering & Imaging Sciences in the Faculty of Life Sciences & Medicine, King's College London. Tiarna’s research focuses on investigating disparities in datasets used to train AI and evaluating fairness and biases in AI used for cardiac imaging.

Mackenzie Jorgensen is a final year PhD candidate in Computer Science at the Informatics Department in the Faculty of Natural Mathematical & Engineering Sciences. Mackenzie’s research focuses on mitigating harm (e.g., discrimination) from automated decision-making systems and taking into consideration the legal implications of those mitigation methods.

Manasi Nandi

Reader in Integrative Pharmacology

Manasi is a scientist and educator based within the Faculty of Life Sciences and Medicine. Her interdisciplinary research focuses on the extraction of new information from vital signs data, aiding research and clinical decision making, and identifying earlier alerts of disease. She has expertise in medicine discovery and development and the translation of laboratory findings into clinical practice. She is a fellow of the British Pharmacological Society, most recently supporting efforts to embed inclusive principles into the national curriculum. Inclusivity and accessibility are at the core of her teaching and research practice. She is the Development, Diversity and Inclusion lead for the School and Cancer and Pharmaceutical Science, supporting EDI activities at Faculty and College level. She launched the Humanising Healthcare seminar series in 2023.

Reading List – Podcasts

Episode 1: AI, Bias and Women

Books

• O'Neil, C (2017) Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Penguin Random House.
• Fry, H (2018) Hello World: How to be Human in the Age of the Machine. Transworld Publishers Ltd.
• Benjamin, R (2019) Race After Technology. Polity.

Articles

• 'What is AI bias?' from IBM
• Cirillo, D., Catuara-Solarz, S., Morey, C. et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digit. Med. 3, 81 (2020).
• Lee, T., Puyol-Antón, E., Ruijsink, B., Aitcheson, K., Shi, M., King, A.P. (2023). An Investigation into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation. In: Wesarg, S., et al. Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. CLIP EPIMI FAIMI 2023 2023 2023. Lecture Notes in Computer Science, vol 14242. Springer, Cham.
• M. Jorgensen et al., Investigating the Legality of Bias Mitigation Methods in the United Kingdom, in IEEE Technology and Society Magazine, vol. 42, no. 4, pp. 87-94, Dec. 2023
• M. Jorgensen et al., Not So Fair: The Impact of Presumably Fair Machine Learning Models | Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, pp. Pages 297–311 Aug 2023

News and other media

• 'Coded Bias' - a documentary on Netflix about bias
• The Meeting of the Minds That Launched AI - IEEE Spectrum on the Dartmouth AI Workshop that kickstarted the AI field
• Algorithmic Justice League - Unmasking AI harms and biases (ajl.org), a pioneering woman researcher and digital activist in the algorithmic justice sphere, Joy Buolamwini, started this league.