| Method cards for prescriptive machine-learning transparency D Adkins, B Alsallakh, A Cheema, N Kokhlikyan, E McReynolds, P Mishra, ... Proceedings of the 1st International Conference on AI Engineering: Software …, 2022 | 20 | 2022 |
| Prescriptive and descriptive approaches to machine-learning transparency D Adkins, B Alsallakh, A Cheema, N Kokhlikyan, E McReynolds, P Mishra, ... CHI conference on human factors in computing systems extended abstracts, 1-9, 2022 | 14 | 2022 |
| Debugging the internals of convolutional networks B Alsallakh, N Kokhlikyan, V Miglani, S Muttepawar, E Wang, S Zhang, ... eXplainable AI approaches for debugging and diagnosis., 2021 | 13 | 2021 |
| System-level transparency of machine learning B Alsallakh, A Cheema, C Procope, D Adkins, E McReynolds, E Wang, ... Technical Report, 2022 | 5 | 2022 |
| Are Convolutional Networks Inherently Foveated? B Alsallakh, V Miglani, N Kokhlikyan, D Adkins, O Reblitz-Richardson SVRHM 2021 Workshop@ NeurIPS, 2021 | 3 | 2021 |
| Bias Mitigation Framework for Intersectional Subgroups in Neural Networks N Kokhlikyan, B Alsallakh, F Wang, V Miglani, OA Yang, D Adkins arXiv preprint arXiv:2212.13014, 2022 | 2 | 2022 |