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 highlight diverse voices from around the globe and foster discussion on building inclusive AI.