Centre for Innovation in Computing at Lassonde

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Laleh Seyyed-Kalantari

Associate Professor, EECS Department
General Member, IC@L
Vector Institute faculty affiliate
VISTA core member
Connected Mind faculty member
Director of Responsible AI Lab.

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2023 – 2024 Research Highlights

Artificial Intelligence (AI) bias and unfairness detection and mitigation

Highlighting and improving the fairness of artificial intelligence (AI) models. Without loss of generality AI in healthcare is one of our main areas of focus. Our goal is to remove barriers toward the trustable deployment of AI models in practice, such that they benefit all users regardless of their membership to different subpopulations of race, age, sex, socioeconomic status, etc. My research vision is to develop novel AI algorithms for fair and precise decision-making. My research in AI fairness in medical imaging has been pioneering in some highly disciplined journals. During 2023-2024, I have been submitting many grant proposals aiming to detect and mitigate bias and unfairness of using AI. I have published 8 papers in journals and, 4 papers in conferences during this period and participated in some invited talks and interviews on this topic. Additionally, we Organized a workshop on Responsible Language Models (ReLM 2024) in AAAI, 2024. Vancouver, BC, Canada. I submitted many grant proposals, and most of them were successful. These include “Toward fair AI in Radiology via Google CXR foundation models”, “AI- EGS”, “tackling AI unfairness through fair dataset curation – AI medical image diagnostic case”.

Research Interests

  • Responsible AI
  • Generative AI in medical imaging
  • Fairness of AI model
  • Foundation models in medical imaging
  • Large Language Models fairness
  • AI risks

Selected Publications

  • “Shortcuts” Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation. I. Banerjee, K. Bhattacharjee, J. L. Burns, H. Trivedi, S. Purkayastha, L. S. Kalantari, B. N. Patel, R. Shiradkar, & J. Gichoya. Journal of the American College of Radiology, Volume 20, Issue 9, September 2023. (IF 5.1)
  • AI Pitfalls. J. W. Gichoya, K. Thomas, L. A. Celi, N. Safdar, I. Banerjee, J. D. Banja, L. S. Kalantari, & H. Trivedi, S. Purkayastha.
  • Association Between Patient Race and Ethnicity and Use of Invasive Ventilation in the United States. F. M. Abdelmalek, F. Angriman, J. Moore, K. Liu, L. Burry, L. S. Kalantari, S. Mehta, J. Gichoya, L. A. Celi, G. Tomlinson, M. Fralick, & C. J. Yarnell. Annals of the American Thoracic Society, Volume 21, Issue 2, Nov. 2023. (IF 8.3)
  • Deep Learning for Computer-Aided Abnormalities Classification in Digital Mammogram: A Data-Centric Perspective. V. Nalla, S. Pouriyeh, R. M. Parizi, H. Trivedi, Q. Z. Sheng, I. Hwang, L. Seyyed-Kalantari, & M. Woo. Current Problems in Diagnostic Radiology, Jan. 2024.
  • Off-Label Drug Use During the COVID-19 Pandemic in Africa: Topic Modelling and Sentiment Analysis of Ivermectin in South Africa and Nigeria as a Case Study. Z. Movahedi Nia, N. L. Bragazzi, A. Ahmadi, A. Asgari, B. Mellado, J. Orbimski, L. Seyyed-Kalantari, W. A. Woldegerima, J. Wu, & J. D. Kong. Journal of the Royal Society Interface, Sep. 2023. (IF 4.8)