Kirk, Hannah Rose

Auditing Large Language Models: A Three-Layered Approach

Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2023) · AI and Ethics

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 292

Auditing large language models: a three-layered approach

Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2023) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 292

Auditing large language models: a three-layered approach

Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2024) · AI and Ethics

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 289

The benefits, risks and bounds of personalizing the alignment of large language models to individuals

Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 122

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Bean, A. M., Kearns, R. O., Romanou, A., Hafner, F. S., Mayne, H., Batzner, J., Foroutan Eghlidi, N., Schmitz, C., Korgul, K., Batra, H., et al. (2025) · Advances in Neural Information Processing Systems 38

Topic ModelingNatural Language Processing Techniques

Cited by 110

Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

Röttger, P., Hofmann, V., Pyatkin, V., Hinck, M., Kirk, H., Schuetze, H., & Hovy, D. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 89

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Röttger, P., Kirk, H., Vidgen, B., Attanasio, G., Bianchi, F., & Hovy, D. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 77

Looking for a handsome carpenter! Debiasing GPT-3 job advertisements

Borchers, C., Gala, D., Gilburt, B., Oravkin, E., Bounsi, W., Asano, Y. M., & Kirk, H. (2022) · Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Ethics and Social Impacts of AITopic Modeling

Cited by 29

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2023) · arXiv

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 22

Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models

Röttger, P., Hofmann, V., Pyatkin, V., Hinck, M., Kirk, H. R., Schütze, H., & Hovy, D. (2024) · arXiv

Ethics and Social Impacts of AISentiment Analysis and Opinion Mining

Cited by 7

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Röttger, P., Kirk, H. R., Vidgen, B., Attanasio, G., Bianchi, F., & Hovy, D. (2023) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

Cited by 6

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen, B., Agrawal, A., Ahmed, A. M., Akinwande, V., Al-Nuaimi, N., Alfaraj, N., Alhajjar, E., Aroyo, L., Bavalatti, T., Bartolo, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 5