| Robustness in machine learning explanations: Does it matter? L Hancox-Li Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 97 | 2020 |
| Epistemic values in feature importance methods: Lessons from feminist epistemology L Hancox-Li, IE Kumar proceedings of the 2021 ACM conference on fairness, accountability, and …, 2021 | 30 | 2021 |
| Idealization and abstraction in models of injustice L Hancox‐Li Hypatia 32 (2), 329-346, 2017 | 9 | 2017 |
| Should attention be all we need? The epistemic and ethical implications of unification in machine learning N Fishman, L Hancox-Li Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 8 | 2022 |
| Making Intelligence: Ethical Values in IQ and ML Benchmarks B Blili-Hamelin, L Hancox-Li Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 5 | 2023 |
| Solutions in constructive field theory L Hancox-Li Philosophy of Science 84 (2), 335-358, 2017 | 5 | 2017 |
| Beyond Methods Reproducibility in Machine Learning L Hancox-Li, C One ML-Retrospectives, Surveys & Meta-Analyses Workshop at NeurIPS, 2020 | 2 | 2020 |
| Unsocial Intelligence: a Pluralistic, Democratic, and Participatory Investigation of AGI Discourse B Blili-Hamelin, L Hancox-Li, A Smart arXiv preprint arXiv:2401.13142, 2024 | | 2024 |
| The Disagreement Problem in Faithfulness Metrics B Barr, N Fatsi, L Hancox-Li, P Richter, D Proano, C Mok arXiv preprint arXiv:2311.07763, 2023 | | 2023 |
| Making Intelligence: Ethics, IQ, and ML Benchmarks B Blili-Hamelin, L Hancox-Li | | 2022 |
