701. Z. Wu, Y. Hu, W. Shi, N. Dziri, A. Suhr, P. Ammanabrolu, N. A. Smith, M. Ostendorf, H. Hajishirzi, "Fine-Grained Human Feedback Gives Better Rewards for Language Model Training" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=CSbGXyCswu.

702.* H. Lee, S. Phatale, H. Mansoor, T. Mesnard, J. Ferret, K. Lu, C. Bishop, E. Hall, V. Carbune, A. Rastogi, S. Prakash, RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback, arXiv:2309.00267 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2309.00267.

703. D. Hadfield-Menell, A. Dragan, P. Abbeel, S. Russell, "Cooperative inverse reinforcement learning" in *Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS'16)* (Curran Associates Inc., 2016) pp. 3916-3924.

704. D. Hadfield-Menell, S. Milli, P. Abbeel, S. Russell, A. D. Dragan, "Inverse reward design" in *Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17)* (Curran Associates Inc., 2017) pp. 6768-6777.

705. X. Liang, K. Shu, K. Lee, P. Abbeel, "Reward Uncertainty for Exploration in Preference-based Reinforcement Learning" in *The 10th International Conference on Learning Representations (ICLR 2022)* (2021). https://openreview.net/forum?id=OWZVD-I-ZrC.

706.* A. Gleave, G. Irving, Uncertainty Estimation for Language Reward Models, arXiv:2203.07472 [cs.CL] (2022). https://doi.org/10.48550/arXiv.2203.07472.

707. A. Rame, G. Couairon, C. Dancette, J.-B. Gaya, M. Shukor, L. Soulier, M. Cord, "Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=ISbbC2VyCu.

708. T. Coste, U. Anwar, R. Kirk, D. Krueger, "Reward Model Ensembles Help Mitigate Overoptimization" in *The 12th International Conference on Learning Representations (ICLR 2024)* (2024). https://openreview.net/forum?id=dcjtMYkpXx.

709.* S. R. Bowman, J. Hyun, E. Perez, E. Chen, C. Pettit, S. Heiner, K. Lukošiūtė, A. Askell, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Olah, D. Amodei, D. Amodei, D. Drain, D. Li, E. Tran-Johnson, ... J. Kaplan, Measuring Progress on Scalable Oversight for Large Language Models, arXiv:2211.03540 [cs.HC] (2022). http://arxiv.org/abs/2211.03540.

710.* C. Burns, P. Izmailov, J. H. Kirchner, B. Baker, L. Gao, L. Aschenbrenner, Y. Chen, A. Ecoffet, M. Joglekar, J. Leike, I. Sutskever, J. Wu, Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision, arXiv:2312.09390 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2312.09390.

711. J. Michael, S. Mahdi, D. Rein, J. Petty, J. Dirani, V. Padmakumar, S. R. Bowman, Debate Helps Supervise Unreliable Experts, arXiv:2311.08702 [cs.AI] (2023). http://arxiv.org/abs/2311.08702.

712.* J. Leike, D. Krueger, T. Everitt, M. Martic, V. Maini, S. Legg, Scalable agent alignment via reward modeling: a research direction, arXiv:1811.07871 [cs.LG] (2018). http://arxiv.org/abs/1811.07871.

713.* A. Khan, J. Hughes, D. Valentine, L. Ruis, K. Sachan, A. Radhakrishnan, E. Grefenstette, S. R. Bowman, T. Rocktäschel, E. Perez, Debating with More Persuasive LLMs Leads to More Truthful Answers, arXiv:2402.06782 [cs.AI] (2024). http://arxiv.org/abs/2402.06782.

714. Z. Li, The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination, arXiv:2304.14347 [cs.CY] (2023). http://arxiv.org/abs/2304.14347.

715.* A. Askell, Y. Bai, A. Chen, D. Drain, D. Ganguli, T. Henighan, A. Jones, N. Joseph, B. Mann, N. DasSarma, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, J. Kernion, K. Ndousse, C. Olsson, D. Amodei, T. Brown, J. Clark, ... J. Kaplan, A General Language Assistant as a Laboratory for Alignment, arXiv:2112.00861 [cs.CL] (2021). http://arxiv.org/abs/2112.00861.

716. P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-T. Yih, T. Rocktäschel, S. Riedel, D. Kiela, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" in *34th Conference on Neural Information Processing Systems (NeurIPS 2020)* (Curran Associates, Inc., 2020) vol. 33, pp. 9459-9474. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html.

717. K. Shuster, S. Poff, M. Chen, D. Kiela, J. Weston, "Retrieval Augmentation Reduces Hallucination in Conversation" in *Findings of the Association for Computational Linguistics: EMNLP 2021* (Association for Computational Linguistics, 2021) pp. 3784-3803. https://doi.org/10.18653/v1/2021.findings-emnlp.320.

718. L. Kuhn, Y. Gal, S. Farquhar, "Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation" in *The 11th International Conference on Learning Representations (ICLR 2023)* (2023). https://openreview.net/forum?id=VD-AYtPOdve.

719. D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, J. Gilmer, "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization" in *2021 IEEE/CVF International Conference on Computer Vision (ICCV)* (2021) pp. 8320-8329. https://doi.org/10.1109/iccv48922.2021.00823.

720. N. Carlini, M. Nasr, C. A. Choquette-Choo, M. Jagielski, I. Gao, P. W. Koh, D. Ippolito, F. Tramèr, L. Schmidt, "Are aligned neural networks adversarially aligned?" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=OQQoD8Vc3B.
701. Z. Wu, Y. Hu, W. Shi, N. Dziri, A. Suhr, P. Ammanabrolu, N. A. Smith, M. Ostendorf, H. Hajishirzi, "Fine-Grained Human Feedback Gives Better Rewards for Language Model Training" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=CSbGXyCswu.

702.* H. Lee, S. Phatale, H. Mansoor, T. Mesnard, J. Ferret, K. Lu, C. Bishop, E. Hall, V. Carbune, A. Rastogi, S. Prakash, RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback, arXiv:2309.00267 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2309.00267.

703. D. Hadfield-Menell, A. Dragan, P. Abbeel, S. Russell, "Cooperative inverse reinforcement learning" in *Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS'16)* (Curran Associates Inc., 2016) pp. 3916-3924.

704. D. Hadfield-Menell, S. Milli, P. Abbeel, S. Russell, A. D. Dragan, "Inverse reward design" in *Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17)* (Curran Associates Inc., 2017) pp. 6768-6777.

705. X. Liang, K. Shu, K. Lee, P. Abbeel, "Reward Uncertainty for Exploration in Preference-based Reinforcement Learning" in *The 10th International Conference on Learning Representations (ICLR 2022)* (2021). https://openreview.net/forum?id=OWZVD-I-ZrC.

706.* A. Gleave, G. Irving, Uncertainty Estimation for Language Reward Models, arXiv:2203.07472 [cs.CL] (2022). https://doi.org/10.48550/arXiv.2203.07472.

707. A. Rame, G. Couairon, C. Dancette, J.-B. Gaya, M. Shukor, L. Soulier, M. Cord, "Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=ISbbC2VyCu.

708. T. Coste, U. Anwar, R. Kirk, D. Krueger, "Reward Model Ensembles Help Mitigate Overoptimization" in *The 12th International Conference on Learning Representations (ICLR 2024)* (2024). https://openreview.net/forum?id=dcjtMYkpXx.

709.* S. R. Bowman, J. Hyun, E. Perez, E. Chen, C. Pettit, S. Heiner, K. Lukošiūtė, A. Askell, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Olah, D. Amodei, D. Amodei, D. Drain, D. Li, E. Tran-Johnson, ... J. Kaplan, Measuring Progress on Scalable Oversight for Large Language Models, arXiv:2211.03540 [cs.HC] (2022). http://arxiv.org/abs/2211.03540.

710.* C. Burns, P. Izmailov, J. H. Kirchner, B. Baker, L. Gao, L. Aschenbrenner, Y. Chen, A. Ecoffet, M. Joglekar, J. Leike, I. Sutskever, J. Wu, Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision, arXiv:2312.09390 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2312.09390.

711. J. Michael, S. Mahdi, D. Rein, J. Petty, J. Dirani, V. Padmakumar, S. R. Bowman, Debate Helps Supervise Unreliable Experts, arXiv:2311.08702 [cs.AI] (2023). http://arxiv.org/abs/2311.08702.

712.* J. Leike, D. Krueger, T. Everitt, M. Martic, V. Maini, S. Legg, Scalable agent alignment via reward modeling: a research direction, arXiv:1811.07871 [cs.LG] (2018). http://arxiv.org/abs/1811.07871.

713.* A. Khan, J. Hughes, D. Valentine, L. Ruis, K. Sachan, A. Radhakrishnan, E. Grefenstette, S. R. Bowman, T. Rocktäschel, E. Perez, Debating with More Persuasive LLMs Leads to More Truthful Answers, arXiv:2402.06782 [cs.AI] (2024). http://arxiv.org/abs/2402.06782.

714. Z. Li, The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucination, arXiv:2304.14347 [cs.CY] (2023). http://arxiv.org/abs/2304.14347.

715.* A. Askell, Y. Bai, A. Chen, D. Drain, D. Ganguli, T. Henighan, A. Jones, N. Joseph, B. Mann, N. DasSarma, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, J. Kernion, K. Ndousse, C. Olsson, D. Amodei, T. Brown, J. Clark, ... J. Kaplan, A General Language Assistant as a Laboratory for Alignment, arXiv:2112.00861 [cs.CL] (2021). http://arxiv.org/abs/2112.00861.

716. P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-T. Yih, T. Rocktäschel, S. Riedel, D. Kiela, "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" in *34th Conference on Neural Information Processing Systems (NeurIPS 2020)* (Curran Associates, Inc., 2020) vol. 33, pp. 9459-9474. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html.

717. K. Shuster, S. Poff, M. Chen, D. Kiela, J. Weston, "Retrieval Augmentation Reduces Hallucination in Conversation" in *Findings of the Association for Computational Linguistics: EMNLP 2021* (Association for Computational Linguistics, 2021) pp. 3784-3803. https://doi.org/10.18653/v1/2021.findings-emnlp.320.

718. L. Kuhn, Y. Gal, S. Farquhar, "Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation" in *The 11th International Conference on Learning Representations (ICLR 2023)* (2023). https://openreview.net/forum?id=VD-AYtPOdve.

719. D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, J. Gilmer, "The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization" in *2021 IEEE/CVF International Conference on Computer Vision (ICCV)* (2021) pp. 8320-8329. https://doi.org/10.1109/iccv.48922.2021.00823.

720. N. Carlini, M. Nasr, C. A. Choquette-Choo, M. Jagielski, I. Gao, P. W. Koh, D. Ippolito, F. Tramèr, L. Schmidt, "Are aligned neural networks adversarially aligned?" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=OQQoD8Vc3B.
721. A. Madry, A. Makelov, L. Schmidt, D. Tsipras, A. Vladu, "Towards Deep Learning Models Resistant to Adversarial Attacks" in *The 6th International Conference on Learning Representations (ICLR 2018)* (2018). https://openreview.net/forum?id=rJzIBfZAb.

722.* E. Hubinger, C. Denison, J. Mu, M. Lambert, M. Tong, M. MacDiarmid, T. Lanham, D. M. Ziegler, T. Maxwell, N. Cheng, A. Jermyn, A. Askell, A. Radhakrishnan, C. Anil, D. Duvenaud, D. Ganguli, F. Barez, J. Clark, K. Ndousse, ... E. Perez, Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, arXiv:2401.05566 [cs.CR] (2024). https://doi.org/10.48550/arXiv.2401.05566.

723. N. Jain, A. Schwarzschild, Y. Wen, G. Somepalli, J. Kirchenbauer, P.-Y. Chiang, M. Goldblum, A. Saha, J. Geiping, T. Goldstein, Baseline Defenses for Adversarial Attacks Against Aligned Language Models, arXiv:2309.00614 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2309.00614.

724. S. Casper, L. Schulze, O. Patel, D. Hadfield-Menell, Defending Against Unforeseen Failure Modes with Latent Adversarial Training, arXiv:2403.05030 [cs.CR] (2024). https://doi.org/10.48550/arXiv.2403.05030.

725. D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, A. Madry, "Robustness May Be at Odds with Accuracy" in *The 7th International Conference on Learning Representations (ICLR 2019)* (2018). https://openreview.net/forum?id=SyxAb30cY7.

726. H. Zhang, Y. Yu, J. Jiao, E. Xing, L. E. Ghaoui, M. Jordan, "Theoretically Principled Trade-off between Robustness and Accuracy" in *Proceedings of the 36th International Conference on Machine Learning* (2019) pp. 7472-7482. https://proceedings.mlr.press/v97/zhang19p.html.

727. Y.-Y. Yang, C. Rashtchian, H. Zhang, R. R. Salakhutdinov, K. Chaudhuri, "A Closer Look at Accuracy vs. Robustness" in *Advances in Neural Information Processing Systems (NeurIPS 2020)* (Curran Associates, Inc., 2020) vol. 33, pp. 8588-8601. https://proceedings.neurips.cc/paper/2020/hash/61d77652c97ef636343742fc3dcf3ba9-Abstract.html.

728. Y. Balaji, T. Goldstein, J. Hoffman, Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets, arXiv:1910.08051 [cs.LG] (2019). https://doi.org/10.48550/arXiv.1910.08051.

729. Y. Wang, D. Zou, J. Yi, J. Bailey, X. Ma, Q. Gu, "Improving Adversarial Robustness Requires Revisiting Misclassified Examples" in *The 8th International Conference on Learning Representations (ICLR 2020)* (2019). https://openreview.net/forum?id=rklOg6EFwS.

730. R. Rade, S.-M. Moosavi-Dezfooli, "Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off" in *The 10th International Conference on Learning Representations (ICLR 2022)* (2021). https://openreview.net/forum?id=Azh9QBQ4tR7.

731. Z. Liu, G. Dou, Z. Tan, Y. Tian, M. Jiang, Towards Safer Large Language Models through Machine Unlearning, arXiv:2402.10058 [cs.CL] (2024). https://doi.org/10.48550/arXiv.2402.10058.

732. S. Liu, Y. Yao, J. Jia, S. Casper, N. Baracaldo, P. Hase, X. Xu, Y. Yao, H. Li, K. R. Varshney, M. Bansal, S. Koyejo, Y. Liu, Rethinking Machine Unlearning for Large Language Models, arXiv:2402.08787 [cs.LG] (2024). https://doi.org/10.48550/arXiv.2402.08787.

733.* R. Eldan, M. Russinovich, Who's Harry Potter? Approximate Unlearning in LLMs, arXiv:2310.02238 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.02238.

734. A. Lynch, P. Guo, A. Ewart, S. Casper, D. Hadfield-Menell, Eight Methods to Evaluate Robust Unlearning in LLMs, arXiv:2402.16835 [cs.CL] (2024). https://doi.org/10.48550/arXiv.2402.16835.

735. G. Alain, Y. Bengio, "Understanding intermediate layers using linear classifier probes" in *5th International Conference on Learning Representations, ICLR 2017* (2017). https://openreview.net/forum?id=HJ4-rAVtI.

736. P. Goyal, A. R. Soriano, C. Hazirbas, L. Sagun, N. Usunier, "Fairness Indicators for Systematic Assessments of Visual Feature Extractors" in *Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22)* (Association for Computing Machinery, 2022) pp. 70-88. https://doi.org/10.1145/3531146.3533074.

737. W. Gurnee, M. Tegmark, "Language Models Represent Space and Time" in *The 12th International Conference on Learning Representations (ICLR 2024)* (2023). https://openreview.net/forum?id=jE8xbmvFin.

738.* S. Marks, M. Tegmark, The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets, arXiv:2310.06824 [cs.Al] (2023). https://doi.org/10.48550/arXiv.2310.06824.

739. A. Ravichander, Y. Belinkov, E. Hovy, "Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?" in *Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (EACL 2021)* (Association for Computational Linguistics, 2021) pp. 3363-3377. https://doi.org/10.18653/v1/2021.eacl-main.295.

740. Y. Elazar, S. Ravfogel, A. Jacovi, Y. Goldberg, Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals. *Trans. Assoc. Comput. Linguist.* **9**, 160-175 (2021). https://doi.org/10.1162/tacl_a_00359.

741. O. Antverg, Y. Belinkov, "On the Pitfalls of Analyzing Individual Neurons in Language Models" in *The 10th International Conference on Learning Representations (ICLR 2022)* (2021). https://openreview.net/forum?id=8uzOEWPQIMu.
721. A. Madry, A. Makelov, L. Schmidt, D. Tsipras, A. Vladu, "Towards Deep Learning Models Resistant to Adversarial Attacks" in *The 6th International Conference on Learning Representations (ICLR 2018)* (2018). https://openreview.net/forum?id=rJzIBfZAAb.

722.* E. Hubinger, C. Denison, J. Mu, M. Lambert, M. Tong, M. MacDiarmid, T. Lanham, D. M. Ziegler, T. Maxwell, N. Cheng, A. Jermyn, A. Askell, A. Radhakrishnan, C. Anil, D. Duvenaud, D. Ganguli, F. Barez, J. Clark, K. Ndousse, ... E. Perez, Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training, arXiv:2401.05566 [cs.CR] (2024). https://doi.org/10.48550/arXiv.2401.05566.

723. N. Jain, A. Schwarzschild, Y. Wen, G. Somepalli, J. Kirchenbauer, P.-Y. Chiang, M. Goldblum, A. Saha, J. Geiping, T. Goldstein, Baseline Defenses for Adversarial Attacks Against Aligned Language Models, arXiv:2309.00614 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2309.00614.

724. S. Casper, L. Schulze, O. Patel, D. Hadfield-Menell, Defending Against Unforeseen Failure Modes with Latent Adversarial Training, arXiv:2403.05030 [cs.CR] (2024). https://doi.org/10.48550/arXiv.2403.05030.

725. D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, A. Madry, "Robustness May Be at Odds with Accuracy" in *The 7th International Conference on Learning Representations (ICLR 2019)* (2018). https://openreview.net/forum?id=SyxAb30cY7.

726. H. Zhang, Y. Yu, J. Jiao, E. Xing, L. E. Ghaoui, M. Jordan, "Theoretically Principled Trade-off between Robustness and Accuracy" in *Proceedings of the 36th International Conference on Machine Learning* (2019) pp. 7472-7482. https://proceedings.mlr.press/v97/zhang19p.html.

727. Y.-Y. Yang, C. Rashtchian, H. Zhang, R. R. Salakhutdinov, K. Chaudhuri, "A Closer Look at Accuracy vs. Robustness" in *Advances in Neural Information Processing Systems (NeurIPS 2020)* (Curran Associates, Inc., 2020) vol. 33, pp. 8588-8601. https://proceedings.neurips.cc/paper/2020/hash/61d77652c97ef636343742fc3dcf3ba9-Abstract.html.

728. Y. Balaji, T. Goldstein, J. Hoffman, Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets, arXiv:1910.08051 [cs.LG] (2019). https://doi.org/10.48550/arXiv.1910.08051.

729. Y. Wang, D. Zou, J. Yi, J. Bailey, X. Ma, Q. Gu, "Improving Adversarial Robustness Requires Revisiting Misclassified Examples" in *The 8th International Conference on Learning Representations (ICLR 2020)* (2019). https://openreview.net/forum?id=rklOg6EFwS.

730. R. Rade, S.-M. Moosavi-Dezfooli, "Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off" in *The 10th International Conference on Learning Representations (ICLR 2022)* (2021). https://openreview.net/forum?id=Azh9QBQ4tR7.

731. Z. Liu, G. Dou, Z. Tan, Y. Tian, M. Jiang, Towards Safer Large Language Models through Machine Unlearning, arXiv:2402.10058 [cs.CL] (2024). https://doi.org/10.48550/arXiv.2402.10058.

732. S. Liu, Y. Yao, J. Jia, S. Casper, N. Baracaldo, P. Hase, X. Xu, Y. Yao, H. Li, K. R. Varshney, M. Bansal, S. Koyejo, Y. Liu, Rethinking Machine Unlearning for Large Language Models, arXiv:2402.08787 [cs.LG] (2024). https://doi.org/10.48550/arXiv.2402.08787.

733.* R. Eldan, M. Russinovich, Who's Harry Potter? Approximate Unlearning in LLMs, arXiv:2310.02238 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.02238.

734. A. Lynch, P. Guo, A. Ewart, S. Casper, D. Hadfield-Menell, Eight Methods to Evaluate Robust Unlearning in LLMs, arXiv:2402.16835 [cs.CL] (2024). https://doi.org/10.48550/arXiv.2402.16835.

735. G. Alain, Y. Bengio, "Understanding intermediate layers using linear classifier probes" in *5th International Conference on Learning Representations, ICLR 2017* (2017). https://openreview.net/forum?id=HJ4-rAVtI.

736. P. Goyal, A. R. Soriano, C. Hazirbas, L. Sagun, N. Usunier, "Fairness Indicators for Systematic Assessments of Visual Feature Extractors" in *Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22)* (Association for Computing Machinery, 2022) pp. 70-88. https://doi.org/10.1145/3531146.3533074.

737. W. Gurnee, M. Tegmark, "Language Models Represent Space and Time" in *The 12th International Conference on Learning Representations (ICLR 2024)* (2023). https://openreview.net/forum?id=jE8xbmvFin.

738.* S. Marks, M. Tegmark, The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets, arXiv:2310.06824 [cs.Al] (2023). https://doi.org/10.48550/arXiv.2310.06824.

739. A. Ravichander, Y. Belinkov, E. Hovy, "Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?" in *Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (EACL 2021)* (Association for Computational Linguistics, 2021) pp. 3363-3377. https://doi.org/10.18653/v1/2021.eacl-main.295.

740. Y. Elazar, S. Ravfogel, A. Jacovi, Y. Goldberg, Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals. *Trans. Assoc. Comput. Linguist.* **9**, 160-175 (2021). https://doi.org/10.1162/tacl_a_00359.

741. O. Antverg, Y. Belinkov, "On the Pitfalls of Analyzing Individual Neurons in Language Models" in *The 10th International Conference on Learning Representations (ICLR 2022)* (2021). https://openreview.net/forum?id=8uzOEWPQIMu.
742. T. Lieberum, M. Rahtz, J. Kramár, N. Nanda, G. Irving, R. Shah, V. Mikulik, Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla, arXiv:2307.09458 [cs.LG] (2023).
http://arxiv.org/abs/2307.09458.

743. E. Mitchell, C. Lin, A. Bosselut, C. D. Manning, C. Finn, "Memory-Based Model Editing at Scale" in Proceedings of the 39th International Conference on Machine Learning (PMLR, 2022) pp. 15817-15831.
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744. K. Meng, D. Bau, A. J. Andonian, Y. Belinkov, "Locating and Editing Factual Associations in GPT" in 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (2022). https://openreview.net/forum?id=-h6WAS6eE4.

745. K. Meng, A. S. Sharma, A. J. Andonian, Y. Belinkov, D. Bau, "Mass-Editing Memory in a Transformer" in The 11th International Conference on Learning Representations (ICLR 2023) (2022).
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746. Y. Gandelsman, A. A. Efros, J. Steinhardt, "Interpreting CLIP's Image Representation via Text-Based Decomposition" in The 12th International Conference on Learning Representations (ICLR 2024) (2023).
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747. C. Tan, G. Zhang, J. Fu, "Massive Editing for Large Language Models via Meta Learning" in The 12th International Conference on Learning Representations (ICLR 2024) (2023). https://openreview.net/forum?id=L6L1CJQ2PE.

748. S. Wang, Y. Zhu, H. Liu, Z. Zheng, C. Chen, J. Li, Knowledge Editing for Large Language Models: A Survey, arXiv:2310.16218 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.16218.

749. Z. Han, C. Gao, J. Liu, J. Zhang, S. Q. Zhang, Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey, arXiv:2403.14608 [cs.LG] (2024). https://doi.org/10.48550/arXiv.2403.14608.

750. X. Wu, J. Li, M. Xu, W. Dong, S. Wu, C. Bian, D. Xiong, "DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models" in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023) (Association for Computational Linguistics, 2023) pp. 2875-2886. https://doi.org/10.18653/v1/2023.emnlp-main.174.

751. K. Li, O. Patel, F. Viégas, H. Pfister, M. Wattenberg, "Inference-Time Intervention: Eliciting Truthful Answers from a Language Model" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023).
https://openreview.net/forum?id=aLLuYpn83y.

752. A. M. Turner, L. Thiergart, D. Udell, G. Leech, U. Mini, M. MacDiarmid, Activation Addition: Steering Language Models Without Optimization, arXiv:2308.10248 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2308.10248.

753. N. Belrose, D. Schneider-Joseph, S. Ravfogel, R. Cotterell, E. Raff, S. Biderman, "LEACE: Perfect linear concept erasure in closed form" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023).
https://openreview.net/forum?id=awIpKpwTwF&notId=Ju4XcafMir.

754. E. Hernandez, B. Z. Li, J. Andreas, Inspecting and Editing Knowledge Representations in Language Models, arXiv:2304.00740 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2304.00740.

755. D. Brown, C. Godfrey, C. Nizinski, J. Tu, H. Kvinge, "Robustness of Edited Neural Networks" in ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo 2023) (2023).
https://openreview.net/forum?id=JAjH6VANZ4.

756. D. Gamage, J. Chen, K. Sasahara, "The Emergence of Deepfakes and its Societal Implications: A Systematic Review" in Proceedings of the 2021 Truth and Trust Online Conference (Hacks Hackers, 2021) pp. 28-39.
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757. A. Kaushal, A. Mina, A. Meena, T. H. Babu, "The societal impact of Deepfakes: Advances in Detection and Mitigation" in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2023) pp. 1-7. https://doi.org/10.1109/icccnt56998.2023.10307353.

758. R. Tang, Y.-N. Chuang, X. Hu, The Science of Detecting LLM-Generated Text. Commun. ACM 67, 50-59 (2024).
https://doi.org/10.1145/3624725.

759. R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, L. Verdoliva, "On The Detection of Synthetic Images Generated by Diffusion Models" in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023) pp. 1-5. https://doi.org/10.1109/icassp49357.2023.10095167.

760. U. Ojha, Y. Li, Y. J. Lee, "Towards Universal Fake Image Detectors that Generalize Across Generative Models" in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE Computer Society, 2023) pp. 24480-24489. https://doi.org/10.1109/cvpr52729.2023.02345.

761. Y. Zhao, T. Pang, C. Du, X. Yang, N.-M. Cheung, M. Lin, A Recipe for Watermarking Diffusion Models, arXiv:2303.10137 [cs.CV] (2023). https://doi.org/10.48550/arXiv.2303.10137.

762. J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, T. Goldstein, "A Watermark for Large Language Models" in Proceedings of the 40th International Conference on Machine Learning (PMLR, 2023) pp. 17061-17084.
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742. T. Lieberum, M. Rahtz, J. Kramár, N. Nanda, G. Irving, R. Shah, V. Mikulik, Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla, arXiv:2307.09458 [cs.LG] (2023).
http://arxiv.org/abs/2307.09458.

743. E. Mitchell, C. Lin, A. Bosselut, C. D. Manning, C. Finn, "Memory-Based Model Editing at Scale" in Proceedings of the 39th International Conference on Machine Learning (PMLR, 2022) pp. 15817-15831.
https://proceedings.mlr.press/v162/mitchell22a.html.

744. K. Meng, D. Bau, A. J. Andonian, Y. Belinkov, "Locating and Editing Factual Associations in GPT" in 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (2022). https://openreview.net/forum?id=-h6WAS6eE4.

745. K. Meng, A. S. Sharma, A. J. Andonian, Y. Belinkov, D. Bau, "Mass-Editing Memory in a Transformer" in The 11th International Conference on Learning Representations (ICLR 2023) (2022).
https://openreview.net/forum?id=MkbcAHlYgyS.

746. Y. Gandelsman, A. A. Efros, J. Steinhardt, "Interpreting CLIP's Image Representation via Text-Based Decomposition" in The 12th International Conference on Learning Representations (ICLR 2024) (2023).
https://openreview.net/forum?id=5Ca9sSzuDp.

747. C. Tan, G. Zhang, J. Fu, "Massive Editing for Large Language Models via Meta Learning" in The 12th International Conference on Learning Representations (ICLR 2024) (2023). https://openreview.net/forum?id=L6L1CJQ2PE.

748. S. Wang, Y. Zhu, H. Liu, Z. Zheng, C. Chen, J. Li, Knowledge Editing for Large Language Models: A Survey, arXiv:2310.16218 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2310.16218.

749. Z. Han, C. Gao, J. Liu, J. Zhang, S. Q. Zhang, Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey, arXiv:2403.14608 [cs.LG] (2024). https://doi.org/10.48550/arXiv.2403.14608.

750. X. Wu, J. Li, M. Xu, W. Dong, S. Wu, C. Bian, D. Xiong, "DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models" in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023) (Association for Computational Linguistics, 2023) pp. 2875-2886. https://doi.org/10.18653/v1/2023.emnlp-main.174.

751. K. Li, O. Patel, F. Viégas, H. Pfister, M. Wattenberg, "Inference-Time Intervention: Eliciting Truthful Answers from a Language Model" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023).
https://openreview.net/forum?id=aLLuYpn83y.

752. A. M. Turner, L. Thiergart, D. Udell, G. Leech, U. Mini, M. MacDiarmid, Activation Addition: Steering Language Models Without Optimization, arXiv:2308.10248 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2308.10248.

753. N. Belrose, D. Schneider-Joseph, S. Ravfogel, R. Cotterell, E. Raff, S. Biderman, "LEACE: Perfect linear concept erasure in closed form" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023).
https://openreview.net/forum?id=awIpKpwTwF&notId=Ju4XcafMir.

754. E. Hernandez, B. Z. Li, J. Andreas, Inspecting and Editing Knowledge Representations in Language Models, arXiv:2304.00740 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2304.00740.

755. D. Brown, C. Godfrey, C. Nizinski, J. Tu, H. Kvinge, "Robustness of Edited Neural Networks" in ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo 2023) (2023).
https://openreview.net/forum?id=JAjH6VANZ4.

756. D. Gamage, J. Chen, K. Sasahara, "The Emergence of Deepfakes and its Societal Implications: A Systematic Review" in Proceedings of the 2021 Truth and Trust Online Conference (Hacks Hackers, 2021) pp. 28-39.
https://www.researchgate.net/publication/355583941_The_Emergence_of_Deepfakes_and_its_Societal_Implications_A_Systematic_Review.

757. A. Kaushal, A. Mina, A. Meena, T. H. Babu, "The societal impact of Deepfakes: Advances in Detection and Mitigation" in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2023) pp. 1-7. https://doi.org/10.1109/icccnt56998.2023.10307353.

758. R. Tang, Y.-N. Chuang, X. Hu, The Science of Detecting LLM-Generated Text. Commun. ACM 67, 50-59 (2024).
https://doi.org/10.1145/3624725.

759. R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, L. Verdoliva, "On The Detection of Synthetic Images Generated by Diffusion Models" in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023) pp. 1-5. https://doi.org/10.1109/icassp49357.2023.10095167.

760. U. Ojha, Y. Li, Y. J. Lee, "Towards Universal Fake Image Detectors that Generalize Across Generative Models" in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE Computer Society, 2023) pp. 24480-24489. https://doi.org/10.1109/cvpr52729.2023.02345.

761. Y. Zhao, T. Pang, C. Du, X. Yang, N.-M. Cheung, M. Lin, A Recipe for Watermarking Diffusion Models, arXiv:2303.10137 [cs.CV] (2023). https://doi.org/10.48550/arXiv.2303.10137.

762. J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, T. Goldstein, "A Watermark for Large Language Models" in Proceedings of the 40th International Conference on Machine Learning (PMLR, 2023) pp. 17061-17084.
https://proceedings.mlr.press/v202/kirchenbauer23a.html.
763. A. Knott, D. Pedreschi, R. Chatila, T. Chakraborti, S. Leavy, R. Baeza-Yates, D. Eyers, A. Trotman, P. D. Teal, P. Biecek, S. Russell, Y. Bengio, Generative AI models should include detection mechanisms as a condition for public release. *Ethics Inf. Technol.* **25**, 55 (2023). https://doi.org/10.1007/s10676-023-09728-4.

764. G. Pang, C. Shen, L. Cao, A. Van Den Hengel, Deep Learning for Anomaly Detection: A Review. *ACM Comput. Surv.* **54**, 381-383 (2021). https://doi.org/10.1145/3439950.

765. T. Ali, P. Kostakos, HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs), arXiv:2309.16021 [cs.CR] (2023). https://doi.org/10.48550/arXiv.2309.16021.

766. J. Geng, F. Cai, Y. Wang, H. Koepp, P. Nakov, I. Gurevych, A Survey of Confidence Estimation and Calibration in Large Language Models, arXiv:2311.08298 [cs.CL] (2023). http://arxiv.org/abs/2311.08298.

767. A. Aldahdooh, W. Hamidouche, S. A. Fezza, O. Déforges, Adversarial example detection for DNN models: a review and experimental comparison. *Artif. Intell. Rev.* **55**, 4403-4462 (2022). https://doi.org/10.1007/s10462-021-10125-w.

768. M. Phute, A. Helbling, M. D. Hull, S. Peng, S. Szyller, C. Cornelius, D. H. Chau, "LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked" in *The Second Tiny Papers Track at ICLR 2024* (2024). https://openreview.net/forum?id=YoqgCIA19o.

769. R. Greenblatt, B. Shlegeris, K. Sachan, F. Roger, AI Control: Improving Safety Despite Intentional Subversion, arXiv:2312.06942 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2312.06942.

770. T. Ploug, S. Holm, "Right to Contest Al Diagnostics Defining Transparency and Explainability Requirements from a Patient's Perspective" in *Artificial Intelligence in Medicine* (Springer Publishing Company, 2022), pp. 227-238.

771. M. Turpin, J. Michael, E. Perez, S. R. Bowman, "Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=bzs4uPLXvi.

772.* A. Radhakrishnan, K. Nguyen, A. Chen, C. Chen, C. Denison, D. Hernandez, E. Durmus, E. Hubinger, J. Kernion, K. Lukošiūtė, N. Cheng, N. Joseph, N. Schiefer, O. Rausch, S. McCandlish, S. El Showk, T. Lanham, T. Maxwell, V. Chandrasekaran, ... E. Perez, Question Decomposition Improves the Faithfulness of Model-Generated Reasoning, arXiv:2307.11768 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2307.11768.

773.* J. Chua, E. Rees, H. Batra, S. R. Bowman, J. Michael, E. Perez, M. Turpin, Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought, arXiv:2403.05518 [cs.CL] (2024). https://doi.org/10.48550/arXiv.2403.05518.

774. A. Saranya, R. Subhashini, A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends. *Decision Analytics Journal* **7**, 100230 (2023). https://doi.org/10.1016/j.dajour.2023.100230.

775. H. Zhao, H. Chen, F. Yang, N. Liu, H. Deng, H. Cai, S. Wang, D. Yin, M. Du, Explainability for large language models: A survey. *ACM Trans. Intell. Syst. Technol.* **15**, 1-38 (2024). https://doi.org/10.1145/3639372.

776. I. Seeber, E. Bittner, R. O. Briggs, T. de Vreede, G.-J. de Vreede, A. Elkins, R. Maier, A. B. Merz, S. Oeste-Reiß, N. Randrup, G. Schwabe, M. Söllner, Machines as teammates: A research agenda on AI in team collaboration. *Inf. Manag.* **57**, 103174 (2020). https://doi.org/10.1016/j.im.2019.103174.

777.* A. Dafoe, E. Hughes, Y. Bachrach, T. Collins, K. R. McKee, J. Z. Leibo, K. Larson, T. Graepel, Open Problems in Cooperative AI, arXiv:2012.08630 [cs.AI] (2020). https://doi.org/10.48550/arXiv.2012.08630.

778. R. Shah, P. Freire, N. Alex, R. Freedman, D. Krasheninnikov, L. Chan, M. D. Dennis, P. Abbeel, A. Dragan, S. Russell, Benefits of Assistance over Reward Learning. (2020). https://openreview.net/forum?id=DFloGDZejiB.

779. A. Dafoe, Y. Bachrach, G. Hadfield, E. Horvitz, K. Larson, T. Graepel, Cooperative AI: machines must learn to find common ground. *Nature* **593**, 33-36 (2021). https://doi.org/10.1038/d41586-021-01170-0.

780. X. Wu, L. Xiao, Y. Sun, J. Zhang, T. Ma, L. He, A survey of human-in-the-loop for machine learning. *Future Gener. Comput. Syst.* **135**, 364-381 (2022). https://doi.org/10.1016/j.future.2022.05.014.

781. J. Cohen, E. Rosenfeld, Z. Kolter, "Certified Adversarial Robustness via Randomized Smoothing" in *Proceedings of the 36th International Conference on Machine Learning* (PMLR, 2019) pp. 1310-1320. https://proceedings.mlr.press/v97/cohen19c.html.

782. N. Carlini, F. Tramèr, K. D. Dvijotham, L. Rice, M. Sun, J. Z. Kolter, "(Certified!!) Adversarial Robustness for Free!" in *The 11th International Conference on Learning Representations (ICLR 2023)* (2022). https://openreview.net/forum?id=JLg5aHHv7j.

783. W. Nie, B. Guo, Y. Huang, C. Xiao, A. Vahdat, A. Anandkumar, "Diffusion Models for Adversarial Purification" in *Proceedings of the 39th International Conference on Machine Learning* (PMLR, 2022) pp. 16805-16827. https://proceedings.mlr.press/v162/nie22a.html.

784. A. Kumar, C. Agarwal, S. Srinivas, A. J. Li, S. Feizi, H. Lakkaraju, Certifying LLM Safety against Adversarial Prompting, arXiv:2309.02705 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2309.02705.
763. A. Knott, D. Pedreschi, R. Chatila, T. Chakraborti, S. Leavy, R. Baeza-Yates, D. Eyers, A. Trotman, P. D. Teal, P. Biecek, S. Russell, Y. Bengio, Generative AI models should include detection mechanisms as a condition for public release. *Ethics Inf. Technol.* **25**, 55 (2023). https://doi.org/10.1007/s10676-023-09728-4.

764. G. Pang, C. Shen, L. Cao, A. Van Den Hengel, Deep Learning for Anomaly Detection: A Review. *ACM Comput. Surv.* **54**, 381-383 (2021). https://doi.org/10.1145/3439950.

765. T. Ali, P. Kostakos, HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs), arXiv:2309.16021 [cs.CR] (2023). https://doi.org/10.48550/arXiv.2309.16021.

766. J. Geng, F. Cai, Y. Wang, H. Koepp, P. Nakov, I. Gurevych, A Survey of Confidence Estimation and Calibration in Large Language Models, arXiv:2311.08298 [cs.CL] (2023). http://arxiv.org/abs/2311.08298.

767. A. Aldahdooh, W. Hamidouche, S. A. Fezza, O. Déforges, Adversarial example detection for DNN models: a review and experimental comparison. *Artif. Intell. Rev.* **55**, 4403-4462 (2022). https://doi.org/10.1007/s10462-021-10125-w.

768. M. Phute, A. Helbling, M. D. Hull, S. Peng, S. Szyller, C. Cornelius, D. H. Chau, "LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked" in *The Second Tiny Papers Track at ICLR 2024* (2024). https://openreview.net/forum?id=YoqgCIA19o.

769. R. Greenblatt, B. Shlegeris, K. Sachan, F. Roger, AI Control: Improving Safety Despite Intentional Subversion, arXiv:2312.06942 [cs.LG] (2023). https://doi.org/10.48550/arXiv.2312.06942.

770. T. Ploug, S. Holm, "Right to Contest AI Diagnostics Defining Transparency and Explainability Requirements from a Patient's Perspective" in *Artificial Intelligence in Medicine* (Springer Publishing Company, 2022), pp. 227-238.

771. M. Turpin, J. Michael, E. Perez, S. R. Bowman, "Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting" in *37th Conference on Neural Information Processing Systems (NeurIPS 2023)* (2023). https://openreview.net/forum?id=bzs4uPLXvi.

772.* A. Radhakrishnan, K. Nguyen, A. Chen, C. Chen, C. Denison, D. Hernandez, E. Durmus, E. Hubinger, J. Kernion, K. Lukošiūtė, N. Cheng, N. Joseph, N. Schiefer, O. Rausch, S. McCandlish, S. El Showk, T. Lanham, T. Maxwell, V. Chandrasekaran, ... E. Perez, Question Decomposition Improves the Faithfulness of Model-Generated Reasoning, arXiv:2307.11768 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2307.11768.

773.* J. Chua, E. Rees, H. Batra, S. R. Bowman, J. Michael, E. Perez, M. Turpin, Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought, arXiv:2403.05518 [cs.CL] (2024). https://doi.org/10.48550/arXiv.2403.05518.

774. A. Saranya, R. Subhashini, A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends. *Decision Analytics Journal* **7**, 100230 (2023). https://doi.org/10.1016/j.dajour.2023.100230.

775. H. Zhao, H. Chen, F. Yang, N. Liu, H. Deng, H. Cai, S. Wang, D. Yin, M. Du, Explainability for large language models: A survey. *ACM Trans. Intell. Syst. Technol.* **15**, 1-38 (2024). https://doi.org/10.1145/3639372.

776. I. Seeber, E. Bittner, R. O. Briggs, T. de Vreede, G.-J. de Vreede, A. Elkins, R. Maier, A. B. Merz, S. Oeste-Reiß, N. Randrup, G. Schwabe, M. Söllner, Machines as teammates: A research agenda on AI in team collaboration. *Inf. Manag.* **57**, 103174 (2020). https://doi.org/10.1016/j.im.2019.103174.

777.* A. Dafoe, E. Hughes, Y. Bachrach, T. Collins, K. R. McKee, J. Z. Leibo, K. Larson, T. Graepel, Open Problems in Cooperative AI, arXiv:2012.08630 [cs.AI] (2020). https://doi.org/10.48550/arXiv.2012.08630.

778. R. Shah, P. Freire, N. Alex, R. Freedman, D. Krasheninnikov, L. Chan, M. D. Dennis, P. Abbeel, A. Dragan, S. Russell, Benefits of Assistance over Reward Learning. (2020). https://openreview.net/forum?id=DFloGDZejiB.

779. A. Dafoe, Y. Bachrach, G. Hadfield, E. Horvitz, K. Larson, T. Graepel, Cooperative AI: machines must learn to find common ground. *Nature* **593**, 33-36 (2021). https://doi.org/10.1038/d41586-021-01170-0.

780. X. Wu, L. Xiao, Y. Sun, J. Zhang, T. Ma, L. He, A survey of human-in-the-loop for machine learning. *Future Gener. Comput. Syst.* **135**, 364-381 (2022). https://doi.org/10.1016/j.future.2022.05.014.

781. J. Cohen, E. Rosenfeld, Z. Kolter, "Certified Adversarial Robustness via Randomized Smoothing" in *Proceedings of the 36th International Conference on Machine Learning* (PMLR, 2019) pp. 1310-1320. https://proceedings.mlr.press/v97/cohen19c.html.

782. N. Carlini, F. Tramèr, K. D. Dvijotham, L. Rice, M. Sun, J. Z. Kolter, "(Certified!!) Adversarial Robustness for Free!" in *The 11th International Conference on Learning Representations (ICLR 2023)* (2022). https://openreview.net/forum?id=JLg5aHHv7j.

783. W. Nie, B. Guo, Y. Huang, C. Xiao, A. Vahdat, A. Anandkumar, "Diffusion Models for Adversarial Purification" in *Proceedings of the 39th International Conference on Machine Learning* (PMLR, 2022) pp. 16805-16827. https://proceedings.mlr.press/v162/nie22a.html.

784. A. Kumar, C. Agarwal, S. Srinivas, A. J. Li, S. Feizi, H. Lakkaraju, Certifying LLM Safety against Adversarial Prompting, arXiv:2309.02705 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2309.02705.
785. A. Zhou, B. Li, H. Wang, "Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks" in *ICLR 2024 Workshop on Secure and Trustworthy Large Language Models* (2024).
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786. J. Babcock, J. Krámar, R. V. Yampolskiy, "Guidelines for Artificial Intelligence Containment" in *Next-Generation Ethics: Engineering a Better Society*, A. E. Abbas, Ed. (Cambridge University Press, Cambridge, 2019), pp. 90–112.

787. J. Banja, J. W. Gichoya, N. Martinez-Martin, L. A. Waller, G. D. Clifford, Fairness as an afterthought: An American perspective on fairness in model developer-clinician user collaborations. *PLOS Digit Health* 2, e0000386 (2023).
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788. N. A. Saxena, K. Huang, E. DeFilippis, G. Radanovic, D. C. Parkes, Y. Liu, "How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness" in *Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES '19)* (Association for Computing Machinery, 2019) pp. 99–106.
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789. W. Fleisher, "What's Fair about Individual Fairness?" in *Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES '21)* (Association for Computing Machinery, 2021) pp. 480–490.
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790. N. A. Saxena, "Perceptions of Fairness" in *Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES '19)* (Association for Computing Machinery, 2019) pp. 537–538. https://doi.org/10.1145/3306618.3314314.

791. R. Binns, "On the apparent conflict between individual and group fairness" in *Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20)* (Association for Computing Machinery, 2020).
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792. N. Lee, Y. Bang, H. Lovenia, S. Cahyawijaya, W. Dai, P. Fung, Survey of Social Bias in Vision-Language Models, arXiv:2309.14381 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2309.14381.

793. N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan, A Survey on Bias and Fairness in Machine Learning. *ACM Comput. Surv.* 54, 1–35 (2021). https://doi.org/10.1145/3457607.

794. X. Ferrer, T. van Nuenen, J. M. Such, M. Coté, N. Criado, Bias and Discrimination in AI: A Cross-Disciplinary Perspective. *IEEE Technol. Soc. Mag.* 40, 72–80 (2021). https://doi.org/10.1109/mts.2021.3056293.

795. R. Schwartz, A. Vassilev, K. Greene, L. Perine, A. Burt, P. Hall, "NIST Special Publication 1270: Towards a standard for identifying and managing bias in artificial intelligence" (National Institute of Standards and Technology (U.S.), 2022); https://doi.org/10.6028/nist.sp.1270.

796. R. Navigli, S. Conia, B. Ross, Biases in Large Language Models: Origins, Inventory, and Discussion. *J. Data and Information Quality* 15, 1–21 (2023). https://doi.org/10.1145/3597307.

797. Y. Li, M. Du, R. Song, X. Wang, Y. Wang, A Survey on Fairness in Large Language Models, arXiv:2308.10149 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2308.10149.

798. M. Georgopoulos, J. Oldfield, M. A. Nicolaou, Y. Panagakis, M. Pantic, Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer. *Int. J. Comput. Vis.* 129, 2288–2307 (2021).
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799. S. Yucer, S. Akçay, N. Al-Moubayed, T. P. Breckon, "Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation" in *2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)* (2020). https://doi.org/10.1109/cvprw50498.2020.00017.

800. D. Jin, Z. Jin, Z. Hu, O. Vechtomova, R. Mihalcea, Deep Learning for Text Style Transfer: A Survey. *Comput. Linguist.* 48, 155–205 (2022). https://doi.org/10.1162/coli_a_00426.

801. A. V. Nadimpalli, A. Rattani, "GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection" in *Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges* (Springer-Verlag, 2023) pp. 320–337. https://doi.org/10.1007/978-3-031-37742-6_25.

802. O. Parraga, M. D. More, C. M. Oliveira, N. S. Gavenski, L. S. Kupssinskü, A. Medronha, L. V. Moura, G. S. Simões, R. C. Barros, Fairness in Deep Learning: A Survey on Vision and Language Research. *ACM Comput. Surv.* (2023).
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803. T. Wang, J. Zhao, M. Yatskar, K.-W. Chang, V. Ordonez, "Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations" in *2019 IEEE/CVF International Conference on Computer Vision (ICCV)* (IEEE, 2019) pp. 5309–5318. https://doi.org/10.1109/iccv.2019.00541.

804. C.-Y. Chuang, Y. Mroueh, "Fair Mixup: Fairness via Interpolation" in *The 9th International Conference on Learning Representations (ICLR 2021)* (2021). https://openreview.net/forum?id=DNI5s5BXeBn.

805. V. S. Lokhande, A. K. Akash, S. N. Ravi, V. Singh, "FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret" in *Computer Vision – ECCV 2020 (ECCV 2020)* (Springer-Verlag, 2020) pp. 365–381.
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785. A. Zhou, B. Li, H. Wang, "Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks" in *ICLR 2024 Workshop on Secure and Trustworthy Large Language Models* (2024).
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786. J. Babcock, J. Krámar, R. V. Yampolskiy, "Guidelines for Artificial Intelligence Containment" in *Next-Generation Ethics: Engineering a Better Society*, A. E. Abbas, Ed. (Cambridge University Press, Cambridge, 2019), pp. 90–112.

787. J. Banja, J. W. Gichoya, N. Martinez-Martin, L. A. Waller, G. D. Clifford, Fairness as an afterthought: An American perspective on fairness in model developer-clinician user collaborations. *PLOS Digit Health* 2, e0000386 (2023).
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788. N. A. Saxena, K. Huang, E. DeFilippis, G. Radanovic, D. C. Parkes, Y. Liu, "How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness" in *Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES '19)* (Association for Computing Machinery, 2019) pp. 99–106.
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789. W. Fleisher, "What's Fair about Individual Fairness?" in *Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES '21)* (Association for Computing Machinery, 2021) pp. 480–490.
https://doi.org/10.1145/3461702.3462621.

790. N. A. Saxena, "Perceptions of Fairness" in *Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES '19)* (Association for Computing Machinery, 2019) pp. 537–538. https://doi.org/10.1145/3306618.3314314.

791. R. Binns, "On the apparent conflict between individual and group fairness" in *Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20)* (Association for Computing Machinery, 2020).
https://doi.org/10.1145/3351095.3372864.

792. N. Lee, Y. Bang, H. Lovenia, S. Cahyawijaya, W. Dai, P. Fung, Survey of Social Bias in Vision-Language Models, arXiv:2309.14381 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2309.14381.

793. N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, A. Galstyan, A Survey on Bias and Fairness in Machine Learning. *ACM Comput. Surv.* 54, 1–35 (2021). https://doi.org/10.1145/3457607.

794. X. Ferrer, T. van Nuenen, J. M. Such, M. Coté, N. Criado, Bias and Discrimination in AI: A Cross-Disciplinary Perspective. *IEEE Technol. Soc. Mag.* 40, 72–80 (2021). https://doi.org/10.1109/mts.2021.3056293.

795. R. Schwartz, A. Vassilev, K. Greene, L. Perine, A. Burt, P. Hall, "NIST Special Publication 1270: Towards a standard for identifying and managing bias in artificial intelligence" (National Institute of Standards and Technology (U.S.), 2022); https://doi.org/10.6028/nist.sp.1270.

796. R. Navigli, S. Conia, B. Ross, Biases in Large Language Models: Origins, Inventory, and Discussion. *J. Data and Information Quality* 15, 1–21 (2023). https://doi.org/10.1145/3597307.

797. Y. Li, M. Du, R. Song, X. Wang, Y. Wang, A Survey on Fairness in Large Language Models, arXiv:2308.10149 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2308.10149.

798. M. Georgopoulos, J. Oldfield, M. A. Nicolaou, Y. Panagakis, M. Pantic, Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer. *Int. J. Comput. Vis.* 129, 2288–2307 (2021).
https://doi.org/10.1007/s11263-021-01448-w.

799. S. Yucer, S. Akçay, N. Al-Moubayed, T. P. Breckon, "Exploring racial bias within face recognition via per-subject adversarially-enabled data augmentation" in *2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)* (2020). https://doi.org/10.1109/cvprw50498.2020.00017.

800. D. Jin, Z. Jin, Z. Hu, O. Vechtomova, R. Mihalcea, Deep Learning for Text Style Transfer: A Survey. *Comput. Linguist.* 48, 155–205 (2022). https://doi.org/10.1162/coli_a_00426.

801. A. V. Nadimpalli, A. Rattani, "GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection" in *Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges* (Springer-Verlag, 2023) pp. 320–337. https://doi.org/10.1007/978-3-031-37742-6_25.

802. O. Parraga, M. D. More, C. M. Oliveira, N. S. Gavenski, L. S. Kupssinskü, A. Medronha, L. V. Moura, G. S. Simões, R. C. Barros, Fairness in Deep Learning: A Survey on Vision and Language Research. *ACM Comput. Surv.* (2023).
https://doi.org/10.1145/3637549.

803. T. Wang, J. Zhao, M. Yatskar, K.-W. Chang, V. Ordonez, "Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations" in *2019 IEEE/CVF International Conference on Computer Vision (ICCV)* (IEEE, 2019) pp. 5309–5318. https://doi.org/10.1109/iccv.2019.00541.

804. C.-Y. Chuang, Y. Mroueh, "Fair Mixup: Fairness via Interpolation" in *The 9th International Conference on Learning Representations (ICLR 2021)* (2021). https://openreview.net/forum?id=DNI5s5BXeBn.

805. V. S. Lokhande, A. K. Akash, S. N. Ravi, V. Singh, "FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret" in *Computer Vision – ECCV 2020 (ECCV 2020)* (Springer-Verlag, 2020) pp. 365–381.
https://doi.org/10.1007/978-3-030-58610-2_22.
806. M. Kearns, S. Neel, A. Roth, Z. S. Wu, "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness" in Proceedings of the 35th International Conference on Machine Learning (PMLR, 2018). https://proceedings.mlr.press/v80/kearns18a.html.

807. N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. D. Laroussilhe, A. Gesmundo, M. Attariyan, S. Gelly, "Parameter-Efficient Transfer Learning for NLP" in Proceedings of the 36th International Conference on Machine Learning (PMLR, 2019). https://proceedings.mlr.press/v97/houlsby19a.html.

808. Z. Fatemi, C. Xing, W. Liu, C. Xiong, "Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting" in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Association for Computational Linguistics, 2023) pp. 1249-1262. https://doi.org/10.18653/v1/2023.acl-short.108.

809. M. Tomalin, B. Byrne, S. Concannon, D. Saunders, S. Ullmann, The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing. Ethics Inf. Technol. 23, 419-433 (2021). https://doi.org/10.1007/s10676-021-09583-1.

810. L. Cheng, A. Mosallanezhad, P. Sheth, H. Liu, "Causal learning for socially responsible AI" in Proceedings of the 30th International Joint Conference on Artificial Intelligence IJCAI-21 (International Joint Conferences on Artificial Intelligence Organization, 2021). https://doi.org/10.24963/ijcai.2021/598.

811. A. Glaese, N. McAleese, M. Trębacz, J. Aslanides, V. Firoiu, T. Ewalds, M. Rauh, L. Weidinger, M. Chadwick, P. Thacker, L. Campbell-Gillingham, J. Uesato, P.-S. Huang, R. Comanescu, F. Yang, A. See, S. Dathathri, R. Greig, C. Chen, . . . G. Irving, "Improving alignment of dialogue agents via targeted human judgements" (Google Deepmind, 2022); https://doi.org/10.48550/arXiv.2209.14375.

812. Z. Yang, L. Li, K. Lin, J. Wang, C.-C. Lin, Z. Liu, L. Wang, The Dawn of LMMs: Preliminary Explorations with GPT-4(ision), arXiv:2309.17421 [cs.CV] (2023). https://doi.org/10.48550/arXiv.2309.17421.

813. Y. Li, C. Zhang, G. Yu, Z. Wang, B. Fu, G. Lin, C. Shen, L. Chen, Y. Wei, StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data, arXiv:2308.10253 [cs.CV] (2023). https://doi.org/10.48550/arXiv.2308.10253.

814. W. Dai, J. Li, D. Li, A. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, S. Hoi, "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). https://openreview.net/forum?id=vvoWPYqZJA.

815. P. Delobelle, B. Berendt, "FairDistillation: Mitigating Stereotyping in Language Models" in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022) (Springer-Verlag, 2023) pp. 638-654. https://doi.org/10.1007/978-3-031-26390-3_37.

816. K. Wang, H. Machiraju, O.-H. Choung, M. Herzog, P. Frossard, CLAD: A Contrastive Learning based Approach for Background Debiasing, arXiv:2210.02748 [cs.CV] (2022). https://doi.org/10.48550/arXiv.2210.02748.

817. M. Zhang, C. R'e, Contrastive adapters for foundation model group robustness. Adv. Neural Inf. Process. Syst. abs/2207.07180 (2022). https://doi.org/10.48550/arXiv.2207.07180.

818. U. Gupta, J. Dhamala, V. Kumar, A. Verma, Y. Pruksachatkun, S. Krishna, R. Gupta, K.-W. Chang, G. Ver Steeg, A. Galstyan, "Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal" in Findings of the Association for Computational Linguistics: ACL 2022 (Association for Computational Linguistics, 2022) pp. 658-678. https://doi.org/10.18653/v1/2022.findings-acl.55.

819. S. Park, K. Choi, H. Yu, Y. Ko, "Never Too Late to Learn: Regularizing Gender Bias in Coreference Resolution" in Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM '23) (Association for Computing Machinery, 2023) pp. 15-23. https://doi.org/10.1145/3539597.3570473.

820.* J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo, W. Manassra, P. Dhariwal, C. Chu, Y. Jiao, A. Ramesh, "Improving image generation with better captions" (OpenAI, 2023).

821.* A. Xiang, Being "Seen" vs. "Mis-Seen": Tensions between Privacy and Fairness in Computer Vision. Harvard Journal of Law & Technology 36 (2022). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4068921.

822. G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, K. Q. Weinberger, "On Fairness and Calibration" in 31st Conference on Neural Information Processing Systems (NIPS 2017) (Curran Associates, Inc., 2017) vol. 30. https://papers.nips.cc/paper_files/paper/2017/hash/b8b9c74ac526fffbeb2d39ab038d1cd7-Abstract.html.

823. Z. Shi, Y. Wang, F. Yin, X. Chen, K.-W. Chang, C.-J. Hsieh, Red teaming language model detectors with language models. Trans. Assoc. Comput. Linguist. 12, 174-189 (2024). https://doi.org/10.1162/tacl_a_00639.

824. Y. Liu, K. Zhang, Y. Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y. Huang, H. Sun, J. Gao, L. He, L. Sun, Sora: A review on background, technology, limitations, and opportunities of large vision models, arXiv:2402.17177 [cs.CV] (2024). https://doi.org/10.48550/arXiv.2402.17177.

825.* S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, A. Awadallah, Orca: Progressive Learning from Complex Explanation Traces of GPT-4, arXiv:2306.02707 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2306.02707.
806. M. Kearns, S. Neel, A. Roth, Z. S. Wu, "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness" in Proceedings of the 35th International Conference on Machine Learning (PMLR, 2018). https://proceedings.mlr.press/v80/kearns18a.html.

807. N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. D. Laroussilhe, A. Gesmundo, M. Attariyan, S. Gelly, "Parameter-Efficient Transfer Learning for NLP" in Proceedings of the 36th International Conference on Machine Learning (PMLR, 2019). https://proceedings.mlr.press/v97/houlsby19a.html.

808. Z. Fatemi, C. Xing, W. Liu, C. Xiong, "Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting" in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Association for Computational Linguistics, 2023) pp. 1249-1262. https://doi.org/10.18653/v1/2023.acl-short.108.

809. M. Tomalin, B. Byrne, S. Concannon, D. Saunders, S. Ullmann, The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing. Ethics Inf. Technol. 23, 419-433 (2021). https://doi.org/10.1007/s10676-021-09583-1.

810. L. Cheng, A. Mosallanezhad, P. Sheth, H. Liu, "Causal learning for socially responsible AI" in Proceedings of the 30th International Joint Conference on Artificial Intelligence IJCAI-21 (International Joint Conferences on Artificial Intelligence Organization, 2021). https://doi.org/10.24963/ijcai.2021/598.

811. A. Glaese, N. McAleese, M. Trębacz, J. Aslanides, V. Firoiu, T. Ewalds, M. Rauh, L. Weidinger, M. Chadwick, P. Thacker, L. Campbell-Gillingham, J. Uesato, P.-S. Huang, R. Comanescu, F. Yang, A. See, S. Dathathri, R. Greig, C. Chen, . . . G. Irving, "Improving alignment of dialogue agents via targeted human judgements" (Google Deepmind, 2022); https://doi.org/10.48550/arXiv.2209.14375.

812. Z. Yang, L. Li, K. Lin, J. Wang, C.-C. Lin, Z. Liu, L. Wang, The Dawn of LMMs: Preliminary Explorations with GPT-4(ision), arXiv:2309.17421 [cs.CV] (2023). https://doi.org/10.48550/arXiv.2309.17421.

813. Y. Li, C. Zhang, G. Yu, Z. Wang, B. Fu, G. Lin, C. Shen, L. Chen, Y. Wei, StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data, arXiv:2308.10253 [cs.CV] (2023). https://doi.org/10.48550/arXiv.2308.10253.

814. W. Dai, J. Li, D. Li, A. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, S. Hoi, "InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning" in 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). https://openreview.net/forum?id=vvoWPYqZJA.

815. P. Delobelle, B. Berendt, "FairDistillation: Mitigating Stereotyping in Language Models" in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022) (Springer-Verlag, 2023) pp. 638-654. https://doi.org/10.1007/978-3-031-26390-3_37.

816. K. Wang, H. Machiraju, O.-H. Choung, M. Herzog, P. Frossard, CLAD: A Contrastive Learning based Approach for Background Debiasing, arXiv:2210.02748 [cs.CV] (2022). https://doi.org/10.48550/arXiv.2210.02748.

817. M. Zhang, C. R'e, Contrastive adapters for foundation model group robustness. Adv. Neural Inf. Process. Syst. abs/2207.07180 (2022). https://doi.org/10.48550/arXiv.2207.07180.

818. U. Gupta, J. Dhamala, V. Kumar, A. Verma, Y. Pruksachatkun, S. Krishna, R. Gupta, K.-W. Chang, G. Ver Steeg, A. Galstyan, "Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal" in Findings of the Association for Computational Linguistics: ACL 2022 (Association for Computational Linguistics, 2022) pp. 658-678. https://doi.org/10.18653/v1/2022.findings-acl.55.

819. S. Park, K. Choi, H. Yu, Y. Ko, "Never Too Late to Learn: Regularizing Gender Bias in Coreference Resolution" in Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM '23) (Association for Computing Machinery, 2023) pp. 15-23. https://doi.org/10.1145/3539597.3570473.

820.* J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo, W. Manassra, P. Dhariwal, C. Chu, Y. Jiao, A. Ramesh, "Improving image generation with better captions" (OpenAI, 2023).

821.* A. Xiang, Being "Seen" vs. "Mis-Seen": Tensions between Privacy and Fairness in Computer Vision. Harvard Journal of Law & Technology 36 (2022). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4068921.

822. G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, K. Q. Weinberger, "On Fairness and Calibration" in 31st Conference on Neural Information Processing Systems (NIPS 2017) (Curran Associates, Inc., 2017) vol. 30. https://papers.nips.cc/paper_files/paper/2017/hash/b8b9c74ac526fffbeb2d39ab038d1cd7-Abstract.html.

823. Z. Shi, Y. Wang, F. Yin, X. Chen, K.-W. Chang, C.-J. Hsieh, Red teaming language model detectors with language models. Trans. Assoc. Comput. Linguist. 12, 174-189 (2024). https://doi.org/10.1162/tacl_a_00639.

824. Y. Liu, K. Zhang, Y. Li, Z. Yan, C. Gao, R. Chen, Z. Yuan, Y. Huang, H. Sun, J. Gao, L. He, L. Sun, Sora: A review on background, technology, limitations, and opportunities of large vision models, arXiv:2402.17177 [cs.CV] (2024). https://doi.org/10.48550/arXiv.2402.17177.

825.* S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, A. Awadallah, Orca: Progressive Learning from Complex Explanation Traces of GPT-4, arXiv:2306.02707 [cs.CL] (2023). https://doi.org/10.48550/arXiv.2306.02707.
826. T. Sorensen, J. Moore, J. Fisher, M. Gordon, N. Miresghallah, C. M. Rytting, A. Ye, L. Jiang, X. Lu, N. Dziri, T. Althoff, Y. Choi, A Roadmap to Pluralistic Alignment, arXiv:2402.05070 [cs.AI] (2024). http://arxiv.org/abs/2402.05070.

827. M. Shur-Ofry, Multiplicity as an AI Governance Principle. (2023). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4444354.

828. A. Chouldechova, Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data **5**, 153-163 (2017). https://doi.org/10.1089/big.2016.0047.

829. J. Kleinberg, "Inherent Trade-Offs in Algorithmic Fairness" in Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '18) (Association for Computing Machinery, 2018) pp. 40. https://doi.org/10.1145/3219617.3219634.

830. Q. Zhang, J. Liu, Z. Zhang, J. Wen, B. Mao, X. Yao, Mitigating Unfairness via Evolutionary Multiobjective Ensemble Learning. IEEE Trans. Evol. Comput. **27**, 848-862 (2023). https://doi.org/10.1109/tevc.2022.3209544.

831. M. Hardt, E. Price, E. Price, N. Srebro, "Equality of Opportunity in Supervised Learning" in 30th Conference on Neural Information Processing Systems (NIPS 2016) (Curran Associates, Inc., 2016) vol. 29. https://proceedings.neurips.cc/paper_files/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html.

832. A. Grover, J. Song, A. Kapoor, K. Tran, A. Agarwal, E. J. Horvitz, S. Ermon, "Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting" in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (Curran Associates, Inc., 2019) vol. 32. https://proceedings.neurips.cc/paper/2019/hash/d76d8deea9c19cc9aaf2237d2bf2f785-Abstract.html.

833. S. Arora, A. Risteski, Y. Zhang, "Do GANs learn the distribution? Some Theory and Empirics" in 6th International Conference on Learning Representations (ICLR) (2018). https://openreview.net/forum?id=BJehNfWO-.

834. D. Zhang, S. Pan, T. Hoang, Z. Xing, M. Staples, X. Xu, L. Yao, Q. Lu, L. Zhu, To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods. AI and Ethics **4**, 83-93 (2024). https://doi.org/10.1007/s43681-023-00398-y.

835. H. Nilforoshan, J. D. Gaebler, R. Shroff, S. Goel, "Causal Conceptions of Fairness and their Consequences" in Proceedings of the 39th International Conference on Machine Learning (ICML 2022) (PMLR, 2022). https://proceedings.mlr.press/v162/nilforoshan22a.html.

836. N. Konstantinov, C. H. Lampert, "On the Impossibility of Fairness-Aware Learning from Corrupted Data" in Algorithmic Fairness through the Lens of Causality and Robustness workshop (AFCR 2021) (PMLR, 2021). https://proceedings.mlr.press/v171/konstantinov22a.html.

837. M. Brcic, R. V. Yampolskiy, Impossibility Results in AI: A Survey. ACM Comput. Surv. **56**, 1-24 (2023). https://doi.org/10.1145/3603371.

838. K. T. Rodolfa, H. Lamba, R. Ghani, Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy. Nature Machine Intelligence **3**, 896-904 (2021). https://doi.org/10.1038/s42256-021-00396-x.

839. B. Green, Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness. Philos. Technol. **35**, 90 (2022). https://doi.org/10.1007/s13347-022-00584-6.

840. A. Bell, L. Bynum, N. Drushchak, T. Zakharchenko, L. Rosenblatt, J. Stoyanovich, "The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice" in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAcT '23) (Association for Computing Machinery, 2023) pp. 400-422. https://doi.org/10.1145/3593013.3594007.

841. C. Hertweck, T. Răz, Gradual (In)Compatibility of Fairness Criteria. AAAI **36**, 11926-11934 (2022). https://doi.org/10.1609/aaai.v36i11.21450.

842. S. Caton, C. Haas, Fairness in Machine Learning: A Survey. ACM Comput. Surv. **56**, 1-38 (2024). https://doi.org/10.1145/3616865.

843. S. Guha, F. A. Khan, J. Stoyanovich, S. Schelter, "Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making" in 2023 IEEE 39th International Conference on Data Engineering (ICDE) (IEEE, 2023) pp. 3747-3754. https://doi.org/10.1109/icde55515.2023.00303.

844. B. Ghai, M. N. Hoque, K. Mueller, "WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings" in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA '21) (Association for Computing Machinery, 2021) pp. 1-7. https://doi.org/10.1145/3411763.3451587.

845. C. Dwork, F. McSherry, K. Nissim, A. Smith, "Calibrating Noise to Sensitivity in Private Data Analysis" in Theory of Cryptography, S. Halevi, T. Rabin, Eds. (Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2006), vol. 3876.

846. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, L. Zhang, "Deep Learning with Differential Privacy" in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS '16) (Association for Computing Machinery, 2016) pp. 308-318. https://doi.org/10.1145/2976749.2978318.
826. T. Sorensen, J. Moore, J. Fisher, M. Gordon, N. Miresghallah, C. M. Rytting, A. Ye, L. Jiang, X. Lu, N. Dziri, T. Althoff, Y. Choi, A Roadmap to Pluralistic Alignment, arXiv:2402.05070 [cs.AI] (2024). http://arxiv.org/abs/2402.05070.

827. M. Shur-Ofry, Multiplicity as an AI Governance Principle. (2023). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4444354.

828. A. Chouldechova, Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data 5, 153-163 (2017). https://doi.org/10.1089/big.2016.0047.

829. J. Kleinberg, "Inherent Trade-Offs in Algorithmic Fairness" in Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '18) (Association for Computing Machinery, 2018) pp. 40. https://doi.org/10.1145/3219617.3219634.

830. Q. Zhang, J. Liu, Z. Zhang, J. Wen, B. Mao, X. Yao, Mitigating Unfairness via Evolutionary Multiobjective Ensemble Learning. IEEE Trans. Evol. Comput. 27, 848-862 (2023). https://doi.org/10.1109/tevc.2022.3209544.

831. M. Hardt, E. Price, E. Price, N. Srebro, "Equality of Opportunity in Supervised Learning" in 30th Conference on Neural Information Processing Systems (NIPS 2016) (Curran Associates, Inc., 2016) vol. 29. https://proceedings.neurips.cc/paper_files/paper/2016/hash/9d2682367c3935defcb1f9e247a97c0d-Abstract.html.

832. A. Grover, J. Song, A. Kapoor, K. Tran, A. Agarwal, E. J. Horvitz, S. Ermon, "Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting" in 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (Curran Associates, Inc., 2019) vol. 32. https://proceedings.neurips.cc/paper/2019/hash/d76d8deea9c19cc9aaf2237d2bf2f785-Abstract.html.

833. S. Arora, A. Risteski, Y. Zhang, "Do GANs learn the distribution? Some Theory and Empirics" in 6th International Conference on Learning Representations (ICLR) (2018). https://openreview.net/forum?id=BJehNfWO-.

834. D. Zhang, S. Pan, T. Hoang, Z. Xing, M. Staples, X. Xu, L. Yao, Q. Lu, L. Zhu, To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods. AI and Ethics 4, 83-93 (2024). https://doi.org/10.1007/s43681-023-00398-y.

835. H. Nilforoshan, J. D. Gaebler, R. Shroff, S. Goel, "Causal Conceptions of Fairness and their Consequences" in Proceedings of the 39th International Conference on Machine Learning (ICML 2022) (PMLR, 2022). https://proceedings.mlr.press/v162/nilforoshan22a.html.

836. N. Konstantinov, C. H. Lampert, "On the Impossibility of Fairness-Aware Learning from Corrupted Data" in Algorithmic Fairness through the Lens of Causality and Robustness workshop (AFCR 2021) (PMLR, 2021). https://proceedings.mlr.press/v171/konstantinov22a.html.

837. M. Brcic, R. V. Yampolskiy, Impossibility Results in AI: A Survey. ACM Comput. Surv. 56, 1-24 (2023). https://doi.org/10.1145/3603371.

838. K. T. Rodolfa, H. Lamba, R. Ghani, Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy. Nature Machine Intelligence 3, 896-904 (2021). https://doi.org/10.1038/s42256-021-00396-x.

839. B. Green, Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness. Philos. Technol. 35, 90 (2022). https://doi.org/10.1007/s13347-022-00584-6.

840. A. Bell, L. Bynum, N. Drushchak, T. Zakharchenko, L. Rosenblatt, J. Stoyanovich, "The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice" in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAcT '23) (Association for Computing Machinery, 2023) pp. 400-422. https://doi.org/10.1145/3593013.3594007.

841. C. Hertweck, T. Răz, Gradual (In)Compatibility of Fairness Criteria. AAAI 36, 11926-11934 (2022). https://doi.org/10.1609/aaai.v36i11.21450.

842. S. Caton, C. Haas, Fairness in Machine Learning: A Survey. ACM Comput. Surv. 56, 1-38 (2024). https://doi.org/10.1145/3616865.

843. S. Guha, F. A. Khan, J. Stoyanovich, S. Schelter, "Automated Data Cleaning Can Hurt Fairness in Machine Learning-based Decision Making" in 2023 IEEE 39th International Conference on Data Engineering (ICDE) (IEEE, 2023) pp. 3747-3754. https://doi.org/10.1109/icde55515.2023.00303.

844. B. Ghai, M. N. Hoque, K. Mueller, "WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings" in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (CHI EA '21) (Association for Computing Machinery, 2021) pp. 1-7. https://doi.org/10.1145/3411763.3451587.

845. C. Dwork, F. McSherry, K. Nissim, A. Smith, "Calibrating Noise to Sensitivity in Private Data Analysis" in Theory of Cryptography, S. Halevi, T. Rabin, Eds. (Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2006), vol. 3876.

846. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, L. Zhang, "Deep Learning with Differential Privacy" in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS '16) (Association for Computing Machinery, 2016) pp. 308-318. https://doi.org/10.1145/2976749.2978318.
847. H. Brown, K. Lee, F. Miresghallah, R. Shokri, F. Tramèr, "What Does it Mean for a Language Model to Preserve Privacy?" in *Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22)* (Association for Computing Machinery, 2022) pp. 2280-2292. https://doi.org/10.1145/3531146.3534642.

848. S. De, L. Berrada, J. Hayes, S. L. Smith, B. Balle, "Unlocking High-Accuracy Differentially Private Image Classification through Scale" (Google Deepmind, 2022); http://arxiv.org/abs/2204.13650.

849. X. Li, F. Tramèr, P. Liang, T. Hashimoto, "Large Language Models Can Be Strong Differentially Private Learners" in *International Conference on Learning Representations 2022* (2022). https://openreview.net/forum?id=bVuP3ItATMz.

850. T. Stadler, B. Oprisanu, C. Troncoso, Synthetic data - anonymisation groundhog day. (USENIX Association, 2022), pp. 1451-1468. https://www.usenix.org/conference/usenixsecurity22/presentation/stadler.

851. M. Meeus, F. Guepin, A.-M. Creţu, Y.-A. de Montjoye, Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing. (Springer Nature Switzerland, 2024), pp. 380-399. https://doi.org/10.1007/978-3-031-51476-0_19.

852. G. Ganev, E. De Cristofaro, On the Inadequacy of Similarity-based Privacy Metrics: Reconstruction Attacks against "Truly Anonymous Synthetic Data", arXiv:2312.05114 [cs.CR] (2023). https://doi.org/10.48550/arXiv.2312.05114.

853. P. Mohassel, Y. Zhang, SecureML: A System for Scalable Privacy-Preserving Machine Learning. (IEEE Computer Society, 2017), pp. 19-38. https://doi.org/10.1109/sp.2017.12.

854. B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y. Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data. (PMLR, 2017), pp. 1273-1282. https://proceedings.mlr.press/v54/mcmahan17a.html.

855. O. Ohrimenko, F. Schuster, C. Fournet, A. Mehta, S. Nowozin, K. Vaswani, M. Costa, Oblivious Multi-Party machine learning on trusted processors. (USENIX Association, 2016), pp. 619-636. https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/ohrimenko.

856. T. Li, E. F. Villaronga, P. Kieseberg, Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten. *Computer Law & Security Review* **34**, 304 (2018). https://scholarship.law.bu.edu/faculty_scholarship/817.

857. A. Ghorbani, J. Zou, "Data Shapley: Equitable Valuation of Data for Machine Learning" in *Proceedings of the 36th International Conference on Machine Learning (ICML 2019)* (PMLR, 2019) vol. 97, pp. 2242-2251. https://proceedings.mlr.press/v97/ghorbani19c.html.

858. M. ElBaih, The Role of Privacy Regulations in AI Development (A Discussion of the Ways in Which Privacy Regulations Can Shape the Development of AI). (2023). https://doi.org/10.2139/ssrn.4589207.

859. A. Cavoukian, Privacy by design: The 7 foundational principles. (2009).

860. European Parliament, Artificial Intelligence Act: deal on comprehensive rules for trustworthy AI (2023). https://www.europarl.europa.eu/news/en/press-room/20231206IPR15699/artificial-intelligence-act-deal-on-comprehensive-rules-for-trustworthy-ai.

861. T. Stadler, B. Kulynych, N. Papernot, M. Gastpar, C. Troncoso, The Fundamental Limits of Least-Privilege Learning. (arXiv, 2024). https://doi.org/10.48550/arXiv.2402.12235.
847. H. Brown, K. Lee, F. Miresghallah, R. Shokri, F. Tramèr, "What Does it Mean for a Language Model to Preserve Privacy?" in *Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22)* (Association for Computing Machinery, 2022) pp. 2280-2292. https://doi.org/10.1145/3531146.3534642.

848. S. De, L. Berrada, J. Hayes, S. L. Smith, B. Balle, "Unlocking High-Accuracy Differentially Private Image Classification through Scale" (Google Deepmind, 2022); http://arxiv.org/abs/2204.13650.

849. X. Li, F. Tramèr, P. Liang, T. Hashimoto, "Large Language Models Can Be Strong Differentially Private Learners" in *International Conference on Learning Representations 2022* (2022). https://openreview.net/forum?id=bVuP3ItATMz.

850. T. Stadler, B. Oprisanu, C. Troncoso, Synthetic data - anonymisation groundhog day. (USENIX Association, 2022), pp. 1451-1468. https://www.usenix.org/conference/usenixsecurity22/presentation/stadler.

851. M. Meeus, F. Guepin, A.-M. Creţu, Y.-A. de Montjoye, Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing. (Springer Nature Switzerland, 2024), pp. 380-399. https://doi.org/10.1007/978-3-031-51476-0_19.

852. G. Ganev, E. De Cristofaro, On the Inadequacy of Similarity-based Privacy Metrics: Reconstruction Attacks against "Truly Anonymous Synthetic Data", arXiv:2312.05114 [cs.CR] (2023). https://doi.org/10.48550/arXiv.2312.05114.

853. P. Mohassel, Y. Zhang, SecureML: A System for Scalable Privacy-Preserving Machine Learning. (IEEE Computer Society, 2017), pp. 19-38. https://doi.org/10.1109/sp.2017.12.

854. B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y. Arcas, Communication-Efficient Learning of Deep Networks from Decentralized Data. (PMLR, 2017), pp. 1273-1282. https://proceedings.mlr.press/v54/mcmahan17a.html.

855. O. Ohrimenko, F. Schuster, C. Fournet, A. Mehta, S. Nowozin, K. Vaswani, M. Costa, Oblivious Multi-Party machine learning on trusted processors. (USENIX Association, 2016), pp. 619-636. https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/ohrimenko.

856. T. Li, E. F. Villaronga, P. Kieseberg, Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten. *Computer Law & Security Review* **34**, 304 (2018). https://scholarship.law.bu.edu/faculty_scholarship/817.

857. A. Ghorbani, J. Zou, "Data Shapley: Equitable Valuation of Data for Machine Learning" in *Proceedings of the 36th International Conference on Machine Learning (ICML 2019)* (PMLR, 2019) vol. 97, pp. 2242-2251. https://proceedings.mlr.press/v97/ghorbani19c.html.

858. M. ElBaih, The Role of Privacy Regulations in AI Development (A Discussion of the Ways in Which Privacy Regulations Can Shape the Development of AI). (2023). https://doi.org/10.2139/ssrn.4589207.

859. A. Cavoukian, Privacy by design: The 7 foundational principles. (2009).

860. European Parliament, Artificial Intelligence Act: deal on comprehensive rules for trustworthy AI (2023). https://www.europarl.europa.eu/news/en/press-room/20231206IPR15699/artificial-intelligence-act-deal-on-comprehensive-rules-for-trustworthy-ai.

861. T. Stadler, B. Kulynych, N. Papernot, M. Gastpar, C. Troncoso, The Fundamental Limits of Least-Privilege Learning. (arXiv, 2024). https://doi.org/10.48550/arXiv.2402.12235.
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Any enquiries regarding this publication should be
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Research series number: DSIT 2024/009

Published in May 2024 by
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© Crown copyright 2024

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21 - 22 MAY 2024
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