Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Authors: Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju | Published: 2019-11-06 | Updated: 2020-02-03
XAI (Explainable AI)
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Authors: Fan Yang, Mengnan Du, Xia Hu | Published: 2019-07-16 | Updated: 2019-08-15
Certifiably Robust Interpretation in Deep Learning
Authors: Alexander Levine, Sahil Singla, Soheil Feizi | Published: 2019-05-28 | Updated: 2019-10-17
Explainable Black-Box Attacks Against Model-based Authentication
Authors: Washington Garcia, Joseph I. Choi, Suman K. Adari, Somesh Jha, Kevin R. B. Butler | Published: 2018-09-28
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Authors: Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres | Published: 2017-11-30 | Updated: 2018-06-07
A Unified Approach to Interpreting Model Predictions
Authors: Scott Lundberg, Su-In Lee | Published: 2017-05-22 | Updated: 2017-11-25
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Authors: Ruth Fong, Andrea Vedaldi | Published: 2017-04-11 | Updated: 2021-12-03
Learning Important Features Through Propagating Activation Differences
Authors: Avanti Shrikumar, Peyton Greenside, Anshul Kundaje | Published: 2017-04-10 | Updated: 2019-10-12