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Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans.
LOF: Identifying Density-Based Local Outliers
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; and Sander, J. 2000 · 2000
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Wine Quality Data Set
UCI. 2009 · 2009
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Distilling the Knowledge in a Neural Network
Hinton, G.; Vinyals, O.; and Dean, J. 2014 · 2014
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Intriguing Properties of Neural Networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2014 · 2014
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
EU. 2016 · 2016
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Deep Counterfactual Networks with Propensity-Dropout
Alaa, A. M.; Weisz, M.; and van der Schaar, M. 2017 · 2017
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Balestriero, R. 2017 · 2017
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COMPAS Dataset
Ofer, D. 2017 · 2017
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Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
Tolomei, G.; Silvestri, F.; Haines, A.; and Lalmas, M. 2017 · 2017
Cited alongside, same era.
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Biggio, B.; and Roli, F. 2018 · 2018
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Adversarial Patch
Brown, T. B.; Mané, D.; Roy, A.; Abadi, M.; and Gilmer, J. 2018 · 2018
Cited alongside, same era.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Dhurandhar, A.; Chen, P.-Y.; Luss, R.; Tu, C.-C.; Ting, P.; Shanmugam, K.; and Das, P. 2018 · 2018
Cited alongside, same era.
Considerations for Evaluation and Generalization in Interpretable Machine Learning , 3–17
Doshi-Velez, F.; and Kim, B. 2018 · 2018
Cited alongside, same era.
Interpretable Credit Application Predictions With Counterfactual Explanations
Efficient Search for Diverse Coherent Explanations
Russell, C. 2019 · 2019
Closest in time.
One Pixel Attack for Fooling Deep Neural Networks
Su, J.; Vargas, D. V.; and Kouichi, S. 2019 · 2019
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Online Shoppers Intention Dataset
UCI. 2019 · 2019
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Actionable Recourse in Linear Classification
Ustun, B.; Spangher, A.; and Liu, Y. 2019 · 2019
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Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems
Joshi, S.; Koyejo, O.; Vijitbenjaronk, W.; Kim, B.; and Ghosh, J. 2020 · 2020
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DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
Kanamori, K.; Takagi, T.; Kobayashi, K.; and Arimura, H. 2020 · 2020
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Grath, R. M.; Costabello, L.; Van, C. L.; Sweeney, P.; Kamiab, F.; Shen, Z.; and Lecue, F. 2018 · 2018
Cited alongside, same era.
Inverse Classification for Comparison-based Interpretability in Machine Learning
Laugel, T.; Lesot, M.-J.; Marsala, C.; Renard, X.; and Detyniecki, M. 2018 · 2018
Cited alongside, same era.
Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
Wachter, S.; Mittelstadt, B.; and Russell, C. 2018 · 2018
Cited alongside, same era.
Deep Neural Decision Trees
Yang, Y.; Morillo, I. G.; and Hospedales, T. M. 2018 · 2018
Cited alongside, same era.
Explainable Reinforcement Learning Through a Causal Lens
Madumal, P.; Miller, T.; Sonenberg, L.; and Vetere, F. 2019 · 2019
Cited alongside, same era.
Explainable Machine Learning Challenge
FICO. 2017a
Cited in the paper.
FICO xML Challenge
FICO. 2017b
Cited in the paper.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Mothilal, R. K.; Sharma, A.; and Tan, C. 2020 · 2020
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FACE: Feasible and Actionable Counterfactual Explanations
Poyiadzi, R.; Sokol, K.; Santos-Rodriguez, R.; De Bie, T.; and Flach, P. 2020 · 2020
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Algorithmic Recourse: From Counterfactual Explanations to Interventions
Karimi, A.-H.; Schölkopf, B.; and Valera, I. 2021 · 2021
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Interpretable Counterfactual Explanations Guided by Prototypes
Van Looveren, A.; and Klaise, J. 2021 · 2021
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