References

Explainable AI and Feature Attribution

  • Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pages 818–833. Springer, 2014.
  • Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, pages 4768–4777, 2017.
  • Piotr Dabkowski and Yarin Gal. Real-time image saliency for black box classifiers. In Advances in Neural Information Processing Systems, pages 6970–6979, 2017.
  • Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1135–1144. ACM, 2016.
  • Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, and Himabindu Lakkaraju. Towards the unification and robustness of perturbation and gradient based explanations. In International Conference on Machine Learning, pages 110–119. PMLR, 2021

Data-centric AI and Data Attribution

  • Amirata Ghorbani and James Zou. Data shapley: Equitable valuation of data for machine learning. In International conference on machine learning, pages 2242–2251. PMLR, 2019.
  • Pang Wei Koh and Percy Liang. Understanding black-box predictions via influence functions. In International conference on machine learning, pages 1885–1894. PMLR, 2017.
  • Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry. Datamodels: Predicting predictions from training data. In International conference on machine learning, pages 9525-9587. PMLR, 2022.
  • Juhan Bae, Wu Lin, Jonathan Lorraine, and Roger Baker Grosse. Training data attribution via approximate unrolling. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

Mechanistic Interpretability and Component Attribution

  • Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems, 35:17359–17372, 2022.
  • Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adri` a Garriga-Alonso. Towards automated circuit discovery for mechanistic interpretability. Advances in Neural Information Processing Systems, 36:16318–16352, 2023.
  • Steven Cao, Victor Sanh, and Alexander Rush. 2021. Low-Complexity Probing via Finding Subnetworks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 960–966, Online. Association for Computational Linguistics.
  • Harshay Shah, Andrew Ilyas, and Aleksander Madry. Decomposing and editing predictions by modeling model computation. In International conference on machine learning, pages 44244-44292. PMLR, 2024.

Others: Explanation Evaluation, Model Editing and Steering, and Explanation Framework

  • Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. Advances in neural information processing systems, 31, 2018.
  • Blair Bilodeau, Natasha Jaques, Pang Wei Koh, and Been Kim. Impossibility theorems for feature attribution. Proceedings of the National Academy of Sciences, 121(2):e2304406120, 2024.
  • Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach to ai transparency. arXiv preprint arXiv:2310.01405, 2023.
  • Tessa Han, Suraj Srinivas, and Himabindu Lakkaraju. Which explanation should i choose? a function approximation perspective to characterizing post hoc explanations. In Advances in Neural Information Processing Systems (NeurIPS), 2022.