Ethical considerations of Generative AI

By AI Generative, D. Alicia, Manuel Lozano, Stefano Masneri, David Pereira Paz, Jacinto Estrecha

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

1. Luke S. Zettlemoyer
2. Jure Leskovec
3. Robin Rombach
4. Björn Ommer
5. Patrick Esser
6. Herv'e J'egou
7. Matthijs Douze
8. Percy Liang

Citations in the corpus

• High-Resolution Image Synthesis with Latent Diffusion Models (2021, background): “In particular, the deliberate manipulation of images (“deep fakes”) is a common problem in this context, and women in particular are disproportionately affected by it [13, 24].”
• Retrieval-Augmented Multimodal Language Modeling (2023, background): “These datasets may contain biases about race, gender and other demographic attributes, and unsafe content such as pornography and violence (Birhane et al., 2021a).”
• The Stable Signature: Rooting Watermarks in Latent Diffusion Models (2023, background): “It opens the doors to new risks like deep fakes, impersonation or copyright usurpation [8, 17].”