Brauner, Jan

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 349

Thousands of AI Authors on the Future of AI

Grace, K., Sandkühler, J. F., Stewart, H., Weinstein-Raun, B., Thomas, S., Stein-Perlman, Z., Salvatier, J., Brauner, J., & Korzekwa, R. C. (2025) · Journal of Artificial Intelligence Research

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 201

Mapping global dynamics of benchmark creation and saturation in artificial intelligence

Ott, S., Barbosa-Silva, A., Blagec, K., Brauner, J., & Samwald, M. (2022) · Nature Communications

scientometrics and bibliometrics researchArtificial Intelligence Applications

Cited by 73

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 46

Measuring Faithfulness in Chain-of-Thought Reasoning

Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., et al. (2023) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 33

The effects of creatine supplementation on cognitive performance—a randomised controlled study

Sandkühler, J. F., Kersting, X., Faust, A., Königs, E. K., Altman, G., Ettinger, U., Lux, S., Philipsen, A., Müller, H., & Brauner, J. (2023) · BMC Medicine

Muscle metabolism and nutritionBiochemical effects in animals

Cited by 29

Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt

Mindermann, S., Brauner, J., Razzak, M., Sharma, M., Kirsch, A., Xu, W., Höltgen, B., Gomez, A. N., Morisot, A., Farquhar, S., et al. (2022) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 28

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning

Radhakrishnan, A., Nguyen, K., Chen, A., Chen, C., Denison, C., Hernandez, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8