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.
Organizers
Aasa Feragen, DTU Compute, Technical University of Denmark
Andrew King, King’s College London
Ben Glocker, Imperial College London
Enzo Ferrante, CONICET, Universidad de Buenos Aires
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, Canada
Bishesh Khanal, NAAMII, Nepal
Ira Ktena, Google DeepMind, UK
Sanmi Koyejo, Stanford University, USA
Karim Lekadir, Universitat de Barcelona, Spain
Mercy Asiedu, Google Research, USA
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
Akshit Achara, King’s College London, UK. Website coordinator
Please direct any inquiries related to this initiative to faimi-organizers@googlegroups.com.