Research on fairness, equity, and accountability in the context of machine learning has extensively demonstrated ethical risks in the deployment of such systems, including for medical image analysis. In a series of interdisciplinary events, our aim is to advance the discourse around fairness issues in the medical image analysis community.


Aasa Feragen, DTU Compute, Technical University of Denmark
Andrew King, King’s College London
Ben Glocker, Imperial College London
Daniel Moyer, Vanderbilt University
Enzo Ferrante, CONICET, Universidad Nacional del Litoral
Eike Petersen, Fraunhofer Institute for Digital Medicine MEVIS, Germany
Esther Puyol-Antón, HeartFlow and King’s College London
Melanie Ganz-Benjaminsen, University of Copenhagen & Neurobiology Research Unit, Rigshospitalet
Veronika Cheplygina, IT University Copenhagen

Advisory Panel members

Judy Gichoya, Emory University, USA
Kanwal Bhatia, Aival, UK
Tal Arbel, McGill University, USA
Bishesh Khanal, NAAMII, Nepal
Ira Ktena, Deep Mind, UK
Sanmi Koyejo, Stanford University, USA
Karim Lekadir, Universitat de Barcelona, Spain

Activity committee

Tareen Dawood, King’s College London, UK. ISBI 2024 tutorial coordinator
Nina Weng, DTU Compute, Technical University of Denmark. ISBI 2024 tutorial coordinator
Tiarna Lee, King’s College London, UK. RISE-FAIMI 2024 summer school coordinator
Dewinda Julianensi Rumala, Institut Teknologi Sepuluh Nopember, Indonesia. YouTube channel coordinator
Emma Stanley, University of Calgary, Canada. YouTube channel coordinator

Please direct any inquiries related to this initiative to