Fetching the paper…
Reading the bibliography…
A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases.
Generalized additive models: some applications
Trevor Hastie and Robert Tibshirani · 1987
Earlier work this paper cites.
Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
Earlier work this paper cites.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Earlier work this paper cites.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Earlier work this paper cites.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Earlier work this paper cites.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Watcher, Brent Mittelstadt, and Chris Russell · 2017
Earlier work this paper cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Earlier work this paper cites.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Earlier work this paper cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
Earlier work this paper cites.
Do explanations make vqa models more predictable to a human?
A Chandrasekaran, V Prabhu, D Yadav, P Chattopadhyay, and D Parikh · 2018
Earlier work this paper cites.
A user study on the effect of aggregating explanations for interpreting machine learning models
J Krause, A Perer, and E Bertini · 2018
Earlier work this paper cites.
Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet Talwalkar · 2018
Earlier work this paper cites.
Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel Goldstein, Jake Hofman, Jennifer Wortman Vaughan, and Hanna Wallach · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Improving recommender systems beyond the algorithm
Tobias Schnabel, Paul N Bennett, and Thorsten Joachims · 2018
Cited alongside, same era.
Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K Ravikumar · 2018
Cited alongside, same era.
What can ai do for me? evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber · 2019
Cited alongside, same era.
Measure utility, gain trust: practical advice for xai researchers
Brittany Davis, Maria Glenski, William Sealy, and Dustin Arendt · 2020
Later among the works it cites.
Evaluating explainable ai: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal · 2020
Later among the works it cites.
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2020
Later among the works it cites.
Problems with shapley-value-based explanations as feature importance measures
I. Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
Later among the works it cites.
A multidisciplinary survey and framework for design and evaluation of explainable ai systems
Sina Mohseni, Niloofar Zarei, and Eric D. Ragan · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
Cited alongside, same era.
An evaluation of the human-interpretability of explanation
I Lage, E Chen, J He, M Narayanan, B Kim, S Gershman, and F Doshi-Velez · 2019
Cited alongside, same era.
Faithful and customizable explanations of black box models
H Lakkaraju, E Kamar, R Caruana, and J Leskovec · 2019
Cited alongside, same era.
Interpretml: A unified framework for machine learning interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana · 2019
Cited alongside, same era.
Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Cited alongside, same era.
BIM: towards quantitative evaluation of interpretability methods with ground truth
Mengjiao Yang and Been Kim · 2019
Cited alongside, same era.
Debugging tests for model explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim · 2020
Cited alongside, same era.
Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind Mothilal, Amit Sharma, and Chenhao Tan · 2020
Later among the works it cites.
Regularizing black-box models for improved interpretability
Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric Xing, and Ameet Talwalkar · 2020
Later among the works it cites.
Evaluating explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Bhuwan Dhingra, Livio Baldini Soares, Michael Collins, Zachary C Lipton, Graham Neubig, and William W Cohen · 2020
Later among the works it cites.
Towards connecting use cases and methods in interpretable machine learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, and Ameet Talwalkar · 2021
Later among the works it cites.
Remembering for the right reasons: Explanations reduce catastrophic forgetting
Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, and trevor darrell · 2021
Later among the works it cites.
How can i choose an explainer? an application-grounded evaluation of post-hoc explanations
Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, and João Gama · 2021
Later among the works it cites.
Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2021
Later among the works it cites.
Sanity simulations for saliency methods
Joon Sik Kim, Gregory Plumb, and Ameet Talwalkar · 2021
Later among the works it cites.
Saliency is a possible red herring when diagnosing poor generalization
Joseph D Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 2021
Later among the works it cites.
Rethinking stability for attribution-based explanations
Chirag Agarwal, Nari Johnson, Martin Pawelczyk, Satyapriya Krishna, Eshika Saxena, Marinka Zitnik, and Himabindu Lakkaraju · 2022
Closest in time.