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This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability.
A value for n-person games
L. S. Shapley · 1953
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Values of Non-Atomic Games
R. J. Aumann and L. S. Shapley · 1974
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Inductive logic programming: Theory and methods
S. Muggleton and L. de Raedt · 1994
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Incorporating prior knowledge in support vector machines for classification: A review
F. Lauer and G. Bloch · 2008
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Understanding web images by object relation network
N. Chen, Q. Y. Zhou, and V. K. Prasanna · 2012
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Witnesses for the Doctor in the Loop
P. Kieseberg, J. Schantl, P. Frühwirt, E. Weippl, and A. Holzinger · 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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An approach to supporting incremental visual data classification
J. G. S. Paiva, W. R. Schwartz, H. Pedrini, and R. Minghim · 2015
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Coactive Learning
P. Shivaswamy and T. Joachims · 2015
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Understanding intra-class knowledge inside CNN
D. Wei, B. Zhou, A. Torrabla, and W. Freeman · 2015
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Understanding Neural Networks Through Deep Visualization
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson · 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), 2016
2016
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Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers
G. Binder, Alexander Montavon, S. Lapuschkin, K.-R. Müller, and W. Samek · 2016
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Interacting with predictions: Visual inspection of black-box machine learning models
J. Krause, A. Perer, and K. Ng · 2016
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Visualizing Deep Convolutional Neural Networks Using Natural Pre-Images
A. Mahendran and A. Vedaldi · 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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GRAM: Graph-based attention model for healthcare representation learning
E. Choi, M. T. Bahadori, L. Song, W. F. Stewart, and J. Sun · 2017
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I. Donadello, F. B. Kessler, and L. Serafini · 2017
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What does explainable AI really mean? a new conceptualization of perspectives
D. Doran, S. Schulz, and T. R. Besold · 2017
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Distilling a Neural Network Into a Soft Decision Tree
N. Frosst and G. E. Hinton · 2017
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Theory-guided data science: A new paradigm for scientific discovery from data
A. Karpatne, G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, and V. Kumar · 2017
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Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
S. Liu, X. Wang, M. Liu, and J. Zhu · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
A. S. Ross, M. C. Hughes, and F. Doshi-Velez · 2017
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W. Samek, T. Wiegand, and K.-R. Müller · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
S. Wachter, B. Mittelstadt, and C. Russell · 2017
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Interactively Transferring CNN Patterns for Part Localization
Q. Zhang, R. Cao, S. Zhang, M. Redmonds, Y. N. Wu, and S.-C. Zhu · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (XAI)
A. Adadi and M. Berrada · 2018
Earlier work this paper cites.
E-SNLI: Natural language inference with natural language explanations
O. M. Camburu, T. Rocktäschel, T. Lukasiewicz, and P. Blunsom · 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, and R. Sayres · 2018
Cited alongside, same era.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Z. C. Lipton · 2018
Cited alongside, same era.
Kame: Knowledge-based attention model for diagnosis prediction in healthcare
F. Ma, Q. You, H. Xiao, R. Chitta, J. Zhou, and J. Gao · 2018
Cited alongside, same era.
Ultra-strong machine learning: comprehensibility of programs learned with ILP
S. H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold · 2018
Cited alongside, same era.
Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al · 2020
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Teaching the Machine to Explain Itself using Domain Knowledge
V. Balayan, P. Saleiro, C. Belém, L. Krippahl, and P. Bizarro · 2020
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eXplainable Cooperative Machine Learning with NOVA
T. Baur, A. Heimerl, F. Lingenfelser, J. Wagner, M. F. Valstar, B. Schuller, and E. André · 2020
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Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models
T. Bikmukhametov and J. Jäschke · 2020
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Toward trustworthy AI development: mechanisms for supporting verifiable claims
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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges , pages 19–36
G. Ras, M. van Gerven, and P. Haselager · 2018
Cited alongside, same era.
Glass-box: Explaining ai decisions with counterfactual statements through conversation with a voice-enabled virtual assistant
K. Sokol and P. Flach · 2018
Cited alongside, same era.
Learning Global Additive Explanations for Neural Nets Using Model Distillation
S. Tan, R. Caruana, G. Hooker, P. Koch, and A. Gordo · 2018
Cited alongside, same era.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning
Z. Bouraoui, A. Cornuéjols, T. Denœux, S. Destercke, D. Dubois, R. Guillaume, J. Marques-Silva, J. Mengin, H. Prade, S. Schockaert, M. Serrurier, and C. Vrain · 2019
Cited alongside, same era.
G. Erion, J. D. Janizek, P. Sturmfels, S. Lundberg, and S.-I. Lee · 2019
Cited alongside, same era.
Tree-based regularization for interpretable readmission prediction
J. Jiang, S. Hewner, and V. Chandola · 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.
M. Brundage, S. Avin, J. Wang, H. Belfield, G. Krueger, G. Hadfield, H. Khlaaf, J. Yang, H. Toner, R. Fong, et al · 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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Directions for explainable knowledge-enabled systems
S. Chari, D. Gruen, O. Seneviratne, and D. McGuinness · 2020
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Concept whitening for interpretable image recognition
Z. Chen, Y. Bei, and C. Rudin · 2020
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Multi-objective counterfactual explanations
S. Dandl, C. Molnar, M. Binder, and B. Bischl · 2020
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
P. Hase and M. Bansal · 2020
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Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning
M. Krishnan · 2020
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A Survey of Data-driven and Knowledge-aware eXplainable AI
X.-H. Li, C. C. Cao, Y. Shi, W. Bai, H. Gao, L. Qiu, C. Wang, Y. Gao, S. Zhang, X. Xue, and L. Chen · 2020
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Pitfalls to Avoid when Interpreting Machine Learning Models
C. Molnar, G. König, J. Herbinger, T. Freiesleben, S. Dandl, C. A. Scholbeck, G. Casalicchio, M. Grosse-Wentrup, and B. Bischl · 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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Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge
L. Rieger, C. Singh, W. Murdoch, and B. Yu · 2020
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Explainable Machine Learning for Scientific Insights and Discoveries
R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke · 2020
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Learning interpretable latent autoencoder representations with annotations of feature sets
S. Rybakov, M. Lotfollahi, F. J. Theis, and F. A. Wolf · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
P. Schramowski, W. Stammer, S. Teso, A. Brugger, F. Herbert, X. Shao, H. G. Luigs, A. K. Mahlein, and K. Kersting · 2020
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One Explanation Does Not Fit All
K. Sokol and P. Flach · 2020
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Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations
W. Stammer, P. Schramowski, and K. Kersting · 2020
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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 · 2020
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A survey on the explainability of supervised machine learning
N. Burkart and M. F. Huber · 2021
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A historical perspective of explainable artificial intelligence
R. Confalonieri, L. Coba, B. Wagner, and T. R. Besold · 2021
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AI explainability: Why one explanation cannot fit all
M. Norkute · 2021
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REM: An integrative rule extraction methodology for explainable data analysis in healthcare
Z. Shams, B. Dimanov, S. Kola, N. Simidjievski, H. A. Terre, P. Scherer, U. Matjasec, J. Abraham, P. Lio, and M. Jamnik · 2021
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A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence
I. Stepin, J. M. Alonso, A. Catala, and M. Pereira-Fariña · 2021
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Informed machine learning - a taxonomy and survey of integrating knowledge into learning systems
L. Von Rueden, S. Mayer, K. Beckh, B. Georgiev, S. Giesselbach, R. Heese, B. Kirsch, J. Pfrommer, A. Pick, R. Ramamurthy, M. Walczak, J. Garcke, C. Bauckhage, and J. Schuecker · 2021
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Teach me to explain: A review of datasets for explainable nlp
S. Wiegreffe and A. Marasović · 2021
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