In this episode we discuss Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness by Felix Friedrich, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Patrick Schramowski, Sasha Luccioni, Kristian Kersting. The paper proposes a strategy called Fair Diffusion to address biases in text-to-image models after deployment. This approach allows users to adjust biases in any direction based on human instructions, enabling the training of generative models on fairness. The authors also conduct an audit of existing text-to-image models for biases and suggest methods to address and mitigate them. Fair Diffusion provides a practical solution for achieving different notions of fairness in generative models.