AI systems should serve all people with diverse values and perspectives around the world. However, as datasets scale, it's widely documented that they exhibit social biases of various forms, which translate to AI systems that cause real-world harm to under-represented demographic groups. A focused investigation of demographic biases in modern foundation models, their real-world impact and mitigation is thus critical to ensure equitable access to future models and their applications. 

This workshop will provide a shared discussion forum between interdisciplinary researchers in machine learning, computer vision, algorithmic fairness, computational ethics, policy of machine learning and human-computer interaction, to foster a common understanding of present demographic biases in foundation models, their impact and mitigation strategies. We will facilitate discussions on diverse aspects of demographic fairness including geographic, cultural, linguistic, socioeconomic, ethnic, gender, race, and disability status, among others.

Workshop took place on June 11th 2025, at the the Music City Center (CVPR 2025 venue) Nashville, TN, USA.

You can check out accepted papers, schedule and the recording of the workshop!

Panelists

• Aishwarya Agrawal, Mila and Deepmind

• Melissa Hall, Meta

• Simran Khanuja, CMU

• Ranjay Krishna, UW

• Megan Richards, NYU

• Angelina Wang, Stanford

Organizers

Please reach out to organizers at demodiv-cvpr@googlegroups.com if you have any questions!