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Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models.
Counterfactual Visual Explanations
Y. Goyal, Z. Wu, J. Ernst, D. Batra, D. Parikh, and S. Lee · 1904
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The magical number seven, plus or minus two: Some limits on our capacity for processing information
G. A. Miller · 1956
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Direct manipulation interfaces
E. L. Hutchins, J. D. Hollan, and D. A. Norman · 1985
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Contrastive explanation
P. Lipton · 1990
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Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches
A. Aamodt and E. Plaza · 1994
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Case-based explanation of non-case-based learning methods
R. Caruana, H. Kangarloo, J. Dionisio, U. Sinha, and D. Johnson · 1999
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Memory and neural network based expert system
C. K. Shin and S. C. Park · 1999
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The impact of the mit-bih arrhythmia database
G. B. Moody and R. G. Mark · 2001
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Trust in automation: Designing for appropriate reliance
J. D. Lee and K. A. See · 2004
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Influence Functions in Deep Learning Are Fragile
S. Basu, P. Pope, and S. Feizi · 2006
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Example-Based Learning in Heuristic Domains: A Cognitive Load Theory Account
A. Renkl, T. Hilbert, and S. Schworm · 2009
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Toward an Instructionally Oriented Theory of Example-Based Learning
A. Renkl · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
A. Bussone, S. Stumpf, and D. O’Sullivan · 2015
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Interactive and Interpretable Machine Learning Models for Human Machine Collaboration
B. Kim · 2015
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Principles of Explanatory Debugging to Personalize Interactive Machine Learning
T. Kulesza, M. Burnett, W.-K. Wong, and S. Stumpf · 2015
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The paradox of automation as anti-bias intervention
I. Ajunwa · 2016
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Examples are not enough, learn to criticize! Criticism for Interpretability
B. Kim, R. Khanna, and O. O. Koyejo · 2016
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”Why Should I Trust You?”: Explaining the Predictions of Any Classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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An automated ecg beat classification system using convolutional neural networks
M. Zubair, J. Kim, and C. Yoon · 2016
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Towards A Rigorous Science of Interpretable Machine Learning
F. Doshi-Velez and B. Kim · 2017
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Designing contestability: Interaction design, machine learning, and mental health
T. Hirsch, K. Merced, S. Narayanan, Z. E. Imel, and D. C. Atkins · 2017
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Artificial intelligence in healthcare: past, present and future
F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang · 2017
Cited alongside, same era.
Understanding Black-box Predictions via Influence Functions
P. W. Koh and P. Liang · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
S. Lundberg and S.-I. Lee · 2017
Cited alongside, same era.
A systematic review and taxonomy of explanations in decision support and recommender systems
I. Nunes and D. Jannach · 2017
Cited alongside, same era.
Explaining Explanations: An Overview of Interpretability of Machine Learning
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal · 2018
Cited alongside, same era.
Interactive analysis of word vector embeddings
F. Heimerl and M. Gleicher · 2018
Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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Shaping our tools: Contestability as a means to promote responsible algorithmic decision making in the professions
D. K. Mulligan, D. Kluttz, and N. Kohli · 2019
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Manipulating and Measuring Model Interpretability
F. Poursabzi-Sangdeh, D. G. Goldstein, J. M. Hofman, J. W. Vaughan, and H. Wallach · 2019
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Fairness and abstraction in sociotechnical systems
A. D. Selbst, D. Boyd, S. A. Friedler, S. Venkatasubramanian, and J. Vertesi · 2019
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Automated and interpretable patient ecg profiles for disease detection, tracking, and discovery
G. H. Tison, J. Zhang, F. N. Delling, and R. C. Deo · 2019
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Cited alongside, same era.
ECG Heartbeat Classification: A Deep Transferable Representation
M. Kachuee, S. Fazeli, and M. Sarrafzadeh · 2018
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al · 2018
Cited alongside, same era.
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
N. Papernot and P. McDaniel · 2018
Cited alongside, same era.
A deep learning approach for ecg-based heartbeat classification for arrhythmia detection
G. Sannino and G. De Pietro · 2018
Cited alongside, same era.
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
S. Wachter, B. Mittelstadt, and C. Russell · 2018
Cited alongside, same era.
LEAFAGE: Example-based and Feature importance-based Explanations for Black-box ML models
A. Adhikari, D. M. J. Tax, R. Satta, and M. Faeth · 2019
Cited alongside, same era.
What clinicians want: Contextualizing explainable machine learning for clinical end use
S. Tonekaboni, S. Joshi, M. D. McCradden, and A. Goldenberg · 2019
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The what-if tool: Interactive probing of machine learning models
J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viégas, and J. Wilson · 2019
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The hidden assumptions behind counterfactual explanations and principal reasons
S. Barocas, A. D. Selbst, and M. Raghavan · 2020
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Explainable machine learning in deployment
U. Bhatt, A. Xiang, S. Sharma, A. Weller, A. Taly, Y. Jia, J. Ghosh, R. Puri, J. M. F. Moura, and P. Eckersley · 2020
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Proxy tasks and subjective measures can be misleading in evaluating explainable ai systems
Z. Buçinca, P. Lin, K. Z. Gajos, and E. L. Glassman · 2020
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Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
S. R. Hong, J. Hullman, and E. Bertini · 2020
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Human factors in model interpretability: Industry practices, challenges, and needs
S. R. Hong, J. Hullman, and E. Bertini · 2020
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Questioning the ai: Informing design practices for explainable ai user experiences
Q. V. Liao, D. Gruen, and S. Miller · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
R. K. Mothilal, A. Sharma, and C. Tan · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
R. K. Mothilal, A. Sharma, and C. Tan · 2020
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Han-ecg: An interpretable atrial fibrillation detection model using hierarchical attention networks
S. Mousavi, F. Afghah, and U. R. Acharya · 2020
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One explanation does not fit all
K. Sokol and P. Flach · 2020
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Visualizing the Impact of Feature Attribution Baselines
P. Sturmfels, S. Lundberg, and S.-I. Lee · 2020
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Misplaced trust: Measuring the interference of machine learning in human decision-making
H. Suresh, N. Lao, and I. Liccardi · 2020
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Do as ai say: susceptibility in deployment of clinical decision-aids
S. Gaube, H. Suresh, M. Raue, A. Merritt, S. J. Berkowitz, E. Lermer, J. F. Coughlin, J. V. Guttag, E. Colak, and M. Ghassemi · 2021
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Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
A. Jacovi, A. Marasović, T. Miller, and Y. Goldberg · 2021
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How can i choose an explainer? an application-grounded evaluation of post-hoc explanations
S. Jesus, C. Belém, V. Balayan, J. Bento, P. Saleiro, P. Bizarro, and J. Gama · 2021
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