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The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain.
Explaining Classifiers with Causal Concept Effect (CaCE)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim · 1907
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
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A value for n-person games
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Estimating causal effects of treatments in randomized and nonrandomized studies
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Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias
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Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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European Union regulations on algorithmic decision-making and a "right to explanation"
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Scott M. Lundberg and Su-In Lee · 2017
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Learning important features through propagating activation differences
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2018
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The mythos of model interpretability
Zachary C. Lipton · 2018
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Neural natural language inference models partially embed theories of lexical entailment and negation
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Peter Hase and Mohit Bansal · 2020
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Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation
Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Emergent linguistic structure in artificial neural networks trained by self-supervision
Christopher D. Manning, Kevin Clark, John Hewitt, Urvashi Khandelwal, and Omer Levy · 2020
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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What does BERT look at? An analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
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The limitations of opaque learning machines
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Proceedings of the fourth BlackboxNLP workshop on analyzing and interpreting neural networks for NLP
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Docogen: Domain counterfactual generation for low resource domain adaptation
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Evaluating explanations: How much do explanations from the teacher aid students?
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