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 FeragenDTU Compute, Technical University of Denmark
- Andrew KingKing's College London
- Ben GlockerImperial College London
- Enzo FerranteCONICET, Universidad de Buenos Aires
- Eike PetersenFraunhofer Institute for Digital Medicine MEVIS, Germany
- Esther Puyol-AntónHeartFlow and King's College London
- Melanie Ganz-BenjaminsenUniversity of Copenhagen & Neurobiology Research Unit, Rigshospitalet
- Veronika CheplyginaIT University Copenhagen
Advisory Panel members
- Judy GichoyaEmory University, USA
- Kanwal BhatiaAival, UK
- Tal ArbelMcGill University, Canada
- Bishesh KhanalNAAMII, Nepal
- Ira KtenaGoogle DeepMind, UK
- Sanmi KoyejoStanford University, USA
- Karim LekadirUniversitat de Barcelona, Spain
- Mercy AsieduGoogle 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 Imperial College London, Canada YouTube channel & MICCAI 2026 coordinator
- Akshit Achara King's College London, UK Website coordinator
- Louisa Fay Stanford University MICCAI 2026 workshop coordinator
- Gelei Xu University of Notre Dame MICCAI 2026 workshop coordinator
- Christopher Thomas Boland DTU Compute, Technical University of Denmark MICCAI 2026 workshop coordinator
Please direct any inquiries related to this initiative to faimi-organizers@googlegroups.com.