% Key papers for Fairness and bias in generative models (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2024_constructing_5480,
  title = {{Constructing Dreams Using Generative AI}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30374},
}

@inproceedings{neurips2023_hard_68349,
  title = {{Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71458},
}

@inproceedings{cvpr2024_openbias_24377,
  title = {{OpenBias: Open-set Bias Detection in Text-to-Image Generative Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31184},
}

@inproceedings{neurips2023_stable_69572,
  title = {{Stable Bias: Evaluating Societal Representations in Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73455},
}

@inproceedings{cvpr2024_can_22948,
  title = {{Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30776},
}

@inproceedings{cvpr2024_cpr_22934,
  title = {{CPR: Retrieval Augmented Generation for Copyright Protection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31313},
}

@inproceedings{cvpr2025_t2isafety_27797,
  title = {{T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34019},
}

@inproceedings{cvpr2024_fairrag_23450,
  title = {{FairRAG: Fair Human Generation via Fair Retrieval Augmentation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29212},
}

@inproceedings{icml2024_quality_43663,
  title = {{Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34789},
}

@inproceedings{cvpr2025_aim_25395,
  title = {{AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34203},
}

@inproceedings{cvpr2025_redefining_27419,
  title = {{Redefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34533},
}

@inproceedings{neurips2023_on_68525,
  title = {{On Measuring Fairness in Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71301},
}

@inproceedings{neurips2025_lineas_75973,
  title = {{LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=EBONa3tT3K},
}

@inproceedings{cvpr2024_va3_25180,
  title = {{VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31286},
}

@inproceedings{cvpr2025_implicit_26597,
  title = {{Implicit Bias Injection Attacks against Text-to-Image Diffusion Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34474},
}

@inproceedings{cvpr2025_k_26674,
  title = {{K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34676},
}

@inproceedings{cvpr2025_training_27944,
  title = {{Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33043},
}

@inproceedings{neurips2024_art_71391,
  title = {{ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95859},
}

@inproceedings{neurips2024_self_73057,
  title = {{Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94368},
}

@inproceedings{aaai2026_test_102575,
  title = {{Test-Time Preference Optimization for Image Restoration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37520},
}

@inproceedings{iclr2024_social_35467,
  title = {{Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17606},
}

@inproceedings{icml2024_intersectional_43927,
  title = {{Intersectional Unfairness Discovery}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34525},
}

@inproceedings{icml2026_surrogateshap_52855,
  title = {{SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Zdx1hFJarp},
}