Slack, Dylan

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 900

Explaining machine learning models with interactive natural language conversations using TalkToModel

Slack, D., Krishna, S., Lakkaraju, H., & Singh, S. (2023) · Nature Machine Intelligence

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 125

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

Lakkaraju, H., Slack, D., Chen, Y., Tan, C., & Singh, S. (2022) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 62

Active meta-learning for predicting and selecting perovskite crystallization experiments

Shekar, V., Nicholas, G., Najeeb, M. A., Zeile, M., Yu, V., Wang, X., Slack, D., Li, Z., Nega, P. W., Chan, E. M., et al. (2022) · The Journal of Chemical Physics

Machine Learning in Materials ScienceMachine Learning and Algorithms

Cited by 25

Post Hoc Explanations of Language Models Can Improve Language Models

Krishna, S., Ma, J., Slack, D., Ghandeharioun, A., Singh, S., & Lakkaraju, H. (2023) · Advances in Neural Information Processing Systems 36

Explainable Artificial Intelligence (XAI)Natural Language Processing Techniques

Cited by 2