Gilbert, Thomas Krendl

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 306

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

Amann, J., Vetter, D., Blomberg, S. N., Christensen, H. C., Coffee, M., Gerke, S., Gilbert, T. K., Hagendorff, T., Holm, S., Livne, M., et al. (2022) · PLOS Digital Health

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 251

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 101

The Future of HCI-Policy Collaboration

Yang, Q., Wong, R. Y., Jackson, S., Junginger, S., Hagan, M. D., Gilbert, T., & Zimmerman, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionInformation Systems Theories and Implementation

Cited by 52

Subjectifying objectivity: Delineating tastes in theoretical quantum gravity research

Gilbert, T. K., & Loveridge, A. (2021) · Social Studies of Science

Philosophy of Biology and Scientific ExplanationRelativity and Gravitational Theory

Cited by 7