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In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field.
W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, B. Yu, Interpretable machine learning: definitions, methods, and applications (2019) · 1901
Earlier work this paper cites.
G. Marra, F. Giannini, M. Diligenti, M. Gori, Integrating learning and reasoning with deep logic models (2019) · 1901
Earlier work this paper cites.
G. D. Hager, A. Drobnis, F. Fang, R. Ghani, A. Greenwald, T. Lyons, D. C. Parkes, J. Schultz, S. Saria, S. F. Smith, M. Tambe, Artificial intelligence for social good (2019) · 1901
Earlier work this paper cites.
A. S. d’Avila Garcez, M. Gori, L. C. Lamb, L. Serafini, M. Spranger, S. N. Tran, Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning (2019) · 1905
Earlier work this paper cites.
M. T. Keane, E. M. Kenny, The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning (2019) · 1905
Earlier work this paper cites.
D. Bouchacourt, L. Denoyer, EDUCE: explaining model decisions through unsupervised concepts extraction (2019) · 1905
Earlier work this paper cites.
D. Leslie, Understanding artificial intelligence ethics and safety (2019) · 1906
Earlier work this paper cites.
E. Tjoa, C. Guan, A survey on explainable artificial intelligence (XAI): Towards medical XAI (2019) · 1907
Earlier work this paper cites.
A. Lucic, H. Haned, M. de Rijke, Explaining predictions from tree-based boosting ensembles (2019) · 1907
Earlier work this paper cites.
R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, G. Cai, N. D. Rodríguez, D. Filliat, DisCoRL: Continual reinforcement learning via policy distillation (2019) · 1907
Earlier work this paper cites.
W. Shang, A. Trott, S. Zheng, C. Xiong, R. Socher, Learning world graphs to accelerate hierarchical reinforcement learning (2019) · 1907
Earlier work this paper cites.
S. Liu, B. Kailkhura, D. Loveland, Y. Han, Generative counterfactual introspection for explainable deep learning (2019) · 1907
Earlier work this paper cites.
L. Rieger, C. Singh, W. J. Murdoch, B. Yu, Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (2019) · 1909
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, S. Riedel, Language models as knowledge bases? (2019) · 1909
Earlier work this paper cites.
R. Benjamins, A. Barbado, D. Sierra, Responsible AI by design (2019) · 1909
Earlier work this paper cites.
M. Cornia, L. Baraldi, R. Cucchiara, Smart: Training shallow memory-aware transformers for robotic explainability (2019) · 1910
Earlier work this paper cites.
H. Chen, S. Lundberg, S.-I. Lee, Explaining models by propagating shapley values of local components (2019) · 1911
Earlier work this paper cites.
S. Basu, K. Kumbier, J. B. Brown, B. Yu, Iterative random forests to discover predictive and stable high-order interactions, Proceedings of the National Academy of Sciences 115 (8) (2018) 1943–1948
1948
Earlier work this paper cites.
B. Kim, C. Rudin, J. A. Shah, The bayesian case model: A generative approach for case-based reasoning and prototype classification, in: Advances in Neural Information Processing Systems, 2014, pp. 1952–1960
1960
Earlier work this paper cites.
H. Laurent, R. L. Rivest, Constructing optimal binary decision trees is Np-complete, Information processing letters 5 (1) (1976) 15–17
1976
Earlier work this paper cites.
R. S. Michalski, A theory and methodology of inductive learning, in: Machine learning, Springer, 1983, pp. 83–134
1983
Earlier work this paper cites.
J. R. Quinlan, Induction of decision trees, Machine learning 1 (1) (1986) 81–106
1986
Earlier work this paper cites.
I. T. Jolliffe, Principal Component Analysis and Factor Analysis, Springer New York, 1986, pp. 115–128
1986
Earlier work this paper cites.
J. R. Quinlan, Simplifying decision trees, International journal of man-machine studies 27 (3) (1987) 221–234
1987
Earlier work this paper cites.
D. Ruppert, Robust statistics: The approach based on influence functions, Taylor & Francis, 1987
1987
Earlier work this paper cites.
J. R. Quinlan, Generating production rules from decision trees., in: ijcai, Vol. 87, Citeseer, 1987, pp. 304–307
1987
Earlier work this paper cites.
P. E. Utgoff, Incremental induction of decision trees, Machine learning 4 (2) (1989) 161–186
1989
Earlier work this paper cites.
G. G. Towell, J. W. Shavlik, Extracting refined rules from knowledge-based neural networks, Machine Learning 13 (1) (1993) 71–101
1993
Earlier work this paper cites.
M. W. Craven, J. W. Shavlik, Using sampling and queries to extract rules from trained neural networks, in: Machine learning proceedings 1994, Elsevier, 1994, pp. 37–45
1994
Earlier work this paper cites.
S. Rovnyak, S. Kretsinger, J. Thorp, D. Brown, Decision trees for real-time transient stability prediction, IEEE Transactions on Power Systems 9 (3) (1994) 1417–1426
1994
Earlier work this paper cites.
L. Fu, Rule generation from neural networks, IEEE Transactions on Systems, Man, and Cybernetics 24 (8) (1994) 1114–1124
1994
Earlier work this paper cites.
S. Thrun, Extracting rules from artificial neural networks with distributed representations, in: Proceedings of the 7th International Conference on Neural Information Processing Systems, NIPS’94, 1994, pp. 505–512
1994
Earlier work this paper cites.
A. Aamodt, E. Plaza, Case-based reasoning: Foundational issues, Methodological Variations, and System Approaches 7 (1) (1994) 39–59
1994
Earlier work this paper cites.
P. Langley, H. A. Simon, Applications of machine learning and rule induction, Communications of the ACM 38 (11) (1995) 54–64
1995
Earlier work this paper cites.
M. W. Craven, Extracting comprehensible models from trained neural networks, Tech. rep., University of Wisconsin-Madison Department of Computer Sciences (1996)
1996
Earlier work this paper cites.
R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological) 58 (1) (1996) 267–288
1996
Earlier work this paper cites.
A. R. Cassandra, L. P. Kaelbling, J. A. Kurien, Acting under uncertainty: Discrete bayesian models for mobile-robot navigation, in: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS’96, Vol. 2, IEEE, 1996, pp. 963–972
1996
Earlier work this paper cites.
M. W. Craven, Extracting comprehensible models from trained neural networks, Ph.D. thesis, aAI9700774 (1996)
1996
Earlier work this paper cites.
A. D. Arbatli, H. L. Akin, Rule extraction from trained neural networks using genetic algorithms, Nonlinear Analysis: Theory, Methods & Applications 30 (3) (1997) 1639–1648
1997
Earlier work this paper cites.
J. M. Benitez, J. L. Castro, I. Requena, Are artificial neural networks black boxes?, IEEE Trans. Neural Networks 8 (5) (1997) 1156–1164
1997
Earlier work this paper cites.
A. B. Tickle, R. Andrews, M. Golea, J. Diederich, The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Transactions on Neural Networks 9 (6) (1998) 1057–1068
1998
Earlier work this paper cites.
G. Ridgeway, D. Madigan, T. Richardson, J. O’Kane, Interpretable boosted naïve bayes classification., in: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1998, pp. 101–104
1998
Earlier work this paper cites.
P. Domingos, Knowledge discovery via multiple models, Intelligent Data Analysis 2 (1-4) (1998) 187–202
1998
Earlier work this paper cites.
U. Hoffrage, G. Gigerenzer, Using natural frequencies to improve diagnostic inferences, Academic medicine 73 (5) (1998) 538–540
1998
Earlier work this paper cites.
M. McAllister, G. Kirkwood, Bayesian stock assessment: a review and example application using the logistic model, ICES Journal of Marine Science 55 (6) (1998) 1031–1060
1998
Earlier work this paper cites.
H. A. Chipman, E. I. George, R. E. McCulloch, Bayesian cart model search, Journal of the American Statistical Association 93 (443) (1998) 935–948
1998
Earlier work this paper cites.
H.-X. Wang, L. Fratiglioni, G. B. Frisoni, M. Viitanen, B. Winblad, Smoking and the occurence of alzheimer’s disease: Cross-sectional and longitudinal data in a population-based study, American journal of epidemiology 149 (7) (1999) 640–644
1999
Earlier work this paper cites.
I. A. Taha, J. Ghosh, Symbolic interpretation of artificial neural networks, IEEE Transactions on Knowledge and Data Engineering 11 (3) (1999) 448–463
1999
Earlier work this paper cites.
G. P. J. Schmitz, C. Aldrich, F. S. Gouws, ANN-DT: an algorithm for extraction of decision trees from artificial neural networks, IEEE Transactions on Neural Networks 10 (6) (1999) 1392–1401
1999
Earlier work this paper cites.
K. Larsen, J. H. Petersen, E. Budtz-Jørgensen, L. Endahl, Interpreting parameters in the logistic regression model with random effects, Biometrics 56 (3) (2000) 909–914
2000
Earlier work this paper cites.
P. Sollich, Probabilistic methods for support vector machines, in: Advances in neural information processing systems, 2000, pp. 349–355
2000
Earlier work this paper cites.
R. Setiono, W. K. Leow, FERNN: An algorithm for fast extraction of rules from neural networks, Applied Intelligence 12 (1) (2000) 15–25
2000
Earlier work this paper cites.
H. Tsukimoto, Extracting rules from trained neural networks, IEEE Transactions on Neural Networks 11 (2) (2000) 377–389
2000
Earlier work this paper cites.
R. Caruana, Case-based explanation for artificial neural nets, in: Artificial Neural Networks in Medicine and Biology, Proceedings of the ANNIMAB-1 Conference, 2000, pp. 303–308
2000
Earlier work this paper cites.
A. Hyvärinen, E. Oja, Oja, e.: Independent component analysis: Algorithms and applications. neural networks 13(4-5), 411-430, Neural networks 13 (2000) 411–430
2000
Earlier work this paper cites.
J. Jaccard, Interaction effects in logistic regression: Quantitative applications in the social sciences, Sage Thousand Oaks, CA, 2001
2001
Earlier work this paper cites.
P. Rothery, D. B. Roy, Application of generalized additive models to butterfly transect count data, Journal of Applied Statistics 28 (7) (2001) 897–909
2001
Earlier work this paper cites.
M. Sato, H. Tsukimoto, Rule extraction from neural networks via decision tree induction, in: IJCNN’01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222), Vol. 3, IEEE, 2001, pp. 1870–1875
2001
Earlier work this paper cites.
B. Smyth, P. McClave, Similarity vs. diversity, in: International conference on case-based reasoning, Springer, 2001, pp. 347–361
2001
Earlier work this paper cites.
J. D. Olden, D. A. Jackson, Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks, Ecological modelling 154 (1-2) (2002) 135–150
2002
Earlier work this paper cites.
C.-Y. J. Peng, T.-S. H. So, F. K. Stage, E. P. S. John, The use and interpretation of logistic regression in higher education journals: 1988–1999, Research in Higher Education 43 (3) (2002) 259–293
2002
Earlier work this paper cites.
H. Núñez, C. Angulo, A. Català, Rule extraction from support vector machines., in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2002, pp. 107–112
2002
Earlier work this paper cites.
C.-Y. J. Peng, K. L. Lee, G. M. Ingersoll, An introduction to logistic regression analysis and reporting, The journal of educational research 96 (1) (2002) 3–14
2002
Earlier work this paper cites.
A. Guisan, T. C. Edwards Jr, T. Hastie, Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecological Modelling 157 (2-3) (2002) 89–100
2002
Earlier work this paper cites.
H. Núñez, C. Angulo, A. Català, Support vector machines with symbolic interpretation, in: VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings., IEEE, 2002, pp. 142–147
2002
Earlier work this paper cites.
P. Sollich, Bayesian methods for support vector machines: Evidence and predictive class probabilities, Machine learning 46 (1-3) (2002) 21–52
2002
Earlier work this paper cites.
R. Féraud, F. Clérot, A methodology to explain neural network classification, Neural networks 15 (2) (2002) 237–246
2002
Earlier work this paper cites.
Z.-H. Zhou, Y. Jiang, S.-F. Chen, Extracting symbolic rules from trained neural network ensembles, AI Communications 16 (1) (2003) 3–15
2003
Earlier work this paper cites.
K. Kelley, B. Clark, V. Brown, J. Sitzia, Good practice in the conduct and reporting of survey research, International Journal for Quality in Health Care 15 (3) (2003) 261–266
2003
Earlier work this paper cites.
X. Fu, C. Ong, S. Keerthi, G. G. Hung, L. Goh, Extracting the knowledge embedded in support vector machines, in: IEEE International Joint Conference on Neural Networks, Vol. 1, IEEE, 2004, pp. 291–296
2004
Earlier work this paper cites.
L. Li, D. M. Umbach, P. Terry, J. A. Taylor, Application of the GA/KNN method to SELDI proteomics data, Bioinformatics 20 (10) (2004) 1638–1640
2004
Earlier work this paper cites.
G. Guo, H. Wang, D. Bell, Y. Bi, K. Greer, An KNN model-based approach and its application in text categorization, in: International Conference on Intelligent Text Processing and Computational Linguistics, Springer, 2004, pp. 559–570
2004
Earlier work this paper cites.
U. Johansson, R. König, L. Niklasson, The truth is in there-rule extraction from opaque models using genetic programming., in: FLAIRS Conference, Miami Beach, FL, 2004, pp. 658–663
2004
Earlier work this paper cites.
U. Johansson, L. Niklasson, R. König, Accuracy vs. comprehensibility in data mining models, in: Proceedings of the seventh international conference on information fusion, Vol. 1, 2004, pp. 295–300
2004
Earlier work this paper cites.
G. Hooker, Discovering additive structure in black box functions, in: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2004, pp. 575–580
2004
Earlier work this paper cites.
G. M. Weiss, Mining with rarity: a unifying framework, ACM Sigkdd Explorations Newsletter 6 (1) (2004) 7–19
2004
Earlier work this paper cites.
H. C. Lane, M. G. Core, M. Van Lent, S. Solomon, D. Gomboc, Explainable artificial intelligence for training and tutoring, Tech. rep., University of Southern California (2005)
2005
Earlier work this paper cites.
F. C. Adriana da Costa, M. M. B. Vellasco, R. Tanscheit, Fuzzy rule extraction from support vector machines, in: International Conference on Hybrid Intelligent Systems, IEEE, 2005, pp. 335–340
2005
Earlier work this paper cites.
B. Haasdonk, Feature space interpretation of SVMs with indefinite kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (4) (2005) 482–492
2005
Earlier work this paper cites.
G. Fung, S. Sandilya, R. B. Rao, Rule extraction from linear support vector machines, in: ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, ACM, 2005, pp. 32–40
2005
Earlier work this paper cites.
Y. Zhang, H. Su, T. Jia, J. Chu, Rule extraction from trained support vector machines, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2005, pp. 61–70
2005
Earlier work this paper cites.
A. Jakulin, M. Možina, J. Demšar, I. Bratko, B. Zupan, Nomograms for visualizing support vector machines, in: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM, 2005, pp. 108–117
2005
Earlier work this paper cites.
U. Johansson, R. König, L. Niklasson, Automatically balancing accuracy and comprehensibility in predictive modeling, in: Proceedings of the 8th International Conference on Information Fusion, Vol. 2, 2005, p. 7 pp
2005
Earlier work this paper cites.
T. A. Etchells, P. J. Lisboa, Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach, IEEE Transactions on Neural Networks 17 (2) (2006) 374–384
2006
Earlier work this paper cites.
H. Núñez, C. Angulo, A. Català, Rule-based learning systems for support vector machines, Neural Processing Letters 24 (1) (2006) 1–18
2006
Earlier work this paper cites.
A. Navia-Vázquez, E. Parrado-Hernández, Support vector machine interpretation, Neurocomputing 69 (13-15) (2006) 1754–1759
2006
Earlier work this paper cites.
P. Rani, C. Liu, N. Sarkar, E. Vanman, An empirical study of machine learning techniques for affect recognition in human–robot interaction, Pattern Analysis and Applications 9 (1) (2006) 58–69
2006
Earlier work this paper cites.
H. Núñez, C. Angulo, A. Català, Rule-based learning systems for support vector machines, Neural Processing Letters 24 (1) (2006) 1–18
2006
Earlier work this paper cites.
C. Buciluǎ, R. Caruana, A. Niculescu-Mizil, Model compression, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2006, pp. 535–541
2006
Earlier work this paper cites.
C. Hofer, M. Denker, S. Ducasse, Design and Implementation of a Backward-In-Time Debugger, in: NODe 2006, Vol. P-88 of Lecture Notes in Informatics, 2006, pp. 17–32
2006
Earlier work this paper cites.
C. C. Fischer, K. J. Tibbetts, D. Morgan, G. Ceder, Predicting crystal structure by merging data mining with quantum mechanics, Nature materials 5 (8) (2006) 641
2006
Earlier work this paper cites.
M. H. Aung, P. G. Lisboa, T. A. Etchells, A. C. Testa, B. Van Calster, S. Van Huffel, L. Valentin, D. Timmerman, Comparing analytical decision support models through boolean rule extraction: A case study of ovarian tumour malignancy, in: International Symposium on Neural Networks, Springer, 2007, pp. 1177–1186
2007
Earlier work this paper cites.
V. Schetinin, J. E. Fieldsend, D. Partridge, T. J. Coats, W. J. Krzanowski, R. M. Everson, T. C. Bailey, A. Hernandez, Confident interpretation of bayesian decision tree ensembles for clinical applications, IEEE Transactions on Information Technology in Biomedicine 11 (3) (2007) 312–319
2007
Earlier work this paper cites.
B. Üstün, W. Melssen, L. Buydens, Visualisation and interpretation of support vector regression models, Analytica Chimica Acta 595 (1-2) (2007) 299–309
2007
Earlier work this paper cites.
N. H. Barakat, A. P. Bradley, Rule extraction from support vector machines: A sequential covering approach, IEEE Transactions on Knowledge and Data Engineering 19 (6) (2007) 729–741
2007
Earlier work this paper cites.
D. Martens, B. Baesens, T. Van Gestel, J. Vanthienen, Comprehensible credit scoring models using rule extraction from support vector machines, European Journal of Operational Research 183 (3) (2007) 1466–1476
2007
Earlier work this paper cites.
D. Berg, Bankruptcy prediction by generalized additive models, Applied Stochastic Models in Business and Industry 23 (2) (2007) 129–143
2007
Earlier work this paper cites.
P. Taylan, G.-W. Weber, A. Beck, New approaches to regression by generalized additive models and continuous optimization for modern applications in finance, science and technology, Optimization 56 (5-6) (2007) 675–698
2007
Earlier work this paper cites.
S.-K. Min, D. Simonis, A. Hense, Probabilistic climate change predictions applying bayesian model averaging, Philosophical transactions of the royal society of london a: mathematical, physical and engineering sciences 365 (1857) (2007) 2103–2116
2007
Earlier work this paper cites.
G. Koop, D. J. Poirier, J. L. Tobias, Bayesian econometric methods, Cambridge University Press, 2007
2007
Earlier work this paper cites.
Z. Chen, J. Li, L. Wei, A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue, Artificial Intelligence in Medicine 41 (2) (2007) 161–175
2007
Earlier work this paper cites.
M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, R. J. Plemmons, Algorithms and applications for approximate nonnegative matrix factorization, Computational Statistics & Data Analysis 52 (2007) 155–173
2007
Earlier work this paper cites.
E. Walter, Cambridge advanced learner’s dictionary, Cambridge University Press, 2008
2008
Earlier work this paper cites.
P. Besnard, A. Hunter, Elements of Argumentation, The MIT Press, 2008
2008
Earlier work this paper cites.
N. Barakat, J. Diederich, Eclectic rule-extraction from support vector machines, International Journal of Computer, Electrical, Automation, Control and Information Engineering 2 (5) (2008) 1672–1675
2008
Earlier work this paper cites.
S.-M. Zhou, J. Q. Gan, Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling, Fuzzy Sets and Systems 159 (23) (2008) 3091–3131
2008
Earlier work this paper cites.
Z. Bursac, C. H. Gauss, D. K. Williams, D. W. Hosmer, Purposeful selection of variables in logistic regression, Source code for biology and medicine 3 (1) (2008) 17
2008
Earlier work this paper cites.
doi:10.1184/R1/6613682.v1
T. L. Griffiths, C. Kemp, J. B. Tenenbaum, Bayesian models of cognition. (4 2008) · 2008
Earlier work this paper cites.
R. Konig, U. Johansson, L. Niklasson, G-rex: A versatile framework for evolutionary data mining, in: 2008 IEEE International Conference on Data Mining Workshops, IEEE, 2008, pp. 971–974
2008
Earlier work this paper cites.
M. Robnik-Šikonja, I. Kononenko, Explaining classifications for individual instances, IEEE Transactions on Knowledge and Data Engineering 20 (5) (2008) 589–600
2008
Earlier work this paper cites.
R. Krishnan, G. Sivakumar, P. Bhattacharya, Extracting decision trees from trained neural networks, Pattern Recognition 32 (12) (1999) 1999–2009
2009
Earlier work this paper cites.
U. Johansson, L. Niklasson, Evolving decision trees using oracle guides, in: 2009 IEEE Symposium on Computational Intelligence and Data Mining, IEEE, 2009, pp. 238–244
2009
Earlier work this paper cites.
J. Pearl, Causality, Cambridge university press, 2009
2009
Earlier work this paper cites.
H. Murase, H. Nagashima, S. Yonezaki, R. Matsukura, T. Kitakado, Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in sendai bay, Japan, ICES Journal of Marine Science 66 (6) (2009) 1417–1424
2009
Earlier work this paper cites.
K. C. Wong, L. Wang, P. Shi, Active model with orthotropic hyperelastic material for cardiac image analysis, in: International Conference on Functional Imaging and Modeling of the Heart, Springer, 2009, pp. 229–238
2009
Earlier work this paper cites.
R. Agrawal, S. Gollapudi, A. Halverson, S. Ieong, Diversifying search results, in: Proceedings of the second ACM international conference on web search and data mining, ACM, 2009, pp. 5–14
2009
Earlier work this paper cites.
S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on knowledge and data engineering 22 (10) (2009) 1345–1359
2009
Earlier work this paper cites.
M. Harbers, K. van den Bosch, J.-J. Meyer, Design and evaluation of explainable BDI agents, in: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 2, IEEE, 2010, pp. 125–132
2010
Earlier work this paper cites.
D. Erhan, A. Courville, Y. Bengio, Understanding representations learned in deep architectures, Department dInformatique et Recherche Operationnelle, University of Montreal, QC, Canada, Tech. Rep 1355 (2010) 1
2010
Earlier work this paper cites.
M. D. Zeiler, D. Krishnan, G. W. Taylor, R. Fergus, Deconvolutional networks., in: CVPR, Vol. 10, 2010, p. 7
2010
Earlier work this paper cites.
C. Mood, Logistic regression: Why we cannot do what we think we can do, and what we can do about it, European sociological review 26 (1) (2010) 67–82
2010
Earlier work this paper cites.
H. Nefeslioglu, E. Sezer, C. Gokceoglu, A. Bozkir, T. Duman, Assessment of landslide susceptibility by decision trees in the metropolitan area of istanbul, turkey, Mathematical Problems in Engineering 2010 (2010) Article ID 901095
2010
Earlier work this paper cites.
B. H. Neelon, A. J. O’Malley, S.-L. T. Normand, A bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use, Statistical modelling 10 (4) (2010) 421–439
2010
Earlier work this paper cites.
I. Kononenko, et al., An efficient explanation of individual classifications using game theory, Journal of Machine Learning Research 11 (Jan) (2010) 1–18
2010
Earlier work this paper cites.
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, K.-R. MÞller, How to explain individual classification decisions, Journal of Machine Learning Research 11 (Jun) (2010) 1803–1831
2010
Earlier work this paper cites.
G. Hautier, C. C. Fischer, A. Jain, T. Mueller, G. Ceder, Finding nature’s missing ternary oxide compounds using machine learning and density functional theory, Chemistry of Materials 22 (12) (2010) 3762–3767
2010
Earlier work this paper cites.
D. Martens, J. Vanthienen, W. Verbeke, B. Baesens, Performance of classification models from a user perspective, Decision Support Systems 51 (4) (2011) 782–793
2011
Earlier work this paper cites.
L. Rosenbaum, G. Hinselmann, A. Jahn, A. Zell, Interpreting linear support vector machine models with heat map molecule coloring, Journal of Cheminformatics 3 (1) (2011) 11
2011
Earlier work this paper cites.
J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen, B. Baesens, An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models, Decision Support Systems 51 (1) (2011) 141–154
2011
Earlier work this paper cites.
A. Pierrot, Y. Goude, Short-term electricity load forecasting with generalized additive models, in: 16th Intelligent System Applications to Power Systems Conference, ISAP 2011, IEEE, 2011, pp. 410–415
2011
Earlier work this paper cites.
G. Synnaeve, P. Bessiere, A bayesian model for opening prediction in RTS games with application to starcraft, in: Computational Intelligence and Games (CIG), 2011 IEEE Conference on, IEEE, 2011, pp. 281–288
2011
Earlier work this paper cites.
P. Cortez, M. J. Embrechts, Opening black box data mining models using sensitivity analysis, in: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2011, pp. 341–348
2011
Earlier work this paper cites.
M. D. Zeiler, G. W. Taylor, R. Fergus, et al., Adaptive deconvolutional networks for mid and high level feature learning., in: ICCV, Vol. 1, 2011, p. 6
2011
Earlier work this paper cites.
A. Vellido, J. D. Martín-Guerrero, P. J. Lisboa, Making machine learning models interpretable., in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Vol. 12, Citeseer, 2012, pp. 163–172
2012
Earlier work this paper cites.
M. G. Augasta, T. Kathirvalavakumar, Reverse engineering the neural networks for rule extraction in classification problems, Neural Processing Letters 35 (2) (2012) 131–150
2012
Earlier work this paper cites.
Y. Lou, R. Caruana, J. Gehrke, Intelligible models for classification and regression, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2012, pp. 150–158
2012
Earlier work this paper cites.
S. Jiang, G. Pang, M. Wu, L. Kuang, An improved k-nearest-neighbor algorithm for text categorization, Expert Systems with Applications 39 (1) (2012) 1503–1509
2012
Earlier work this paper cites.
R. Calabrese, et al., Estimating bank loans loss given default by generalized additive models, UCD Geary Institute Discussion Paper Series, WP2012/24 (2012)
2012
Earlier work this paper cites.
L. Auret, C. Aldrich, Interpretation of nonlinear relationships between process variables by use of random forests, Minerals Engineering 35 (2012) 27–42
2012
Earlier work this paper cites.
F. Kamiran, T. Calders, Data preprocessing techniques for classification without discrimination, Knowledge and Information Systems 33 (1) (2012) 1–33
2012
Earlier work this paper cites.
J. Díez, K. Khalifa, B. Leuridan, General theories of explanation: buyer beware, Synthese 190 (3) (2013) 379–396
2013
Earlier work this paper cites.
Y. Lou, R. Caruana, J. Gehrke, G. Hooker, Accurate intelligible models with pairwise interactions, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2013, pp. 623–631
2013
Earlier work this paper cites.
R. A. Berk, J. Bleich, Statistical procedures for forecasting criminal behavior: A comparative assessment, Criminology & Public Policy 12 (3) (2013) 513–544
2013
Cited alongside, same era.
M. Kuhn, K. Johnson, Applied predictive modeling, Vol. 26, Springer, 2013
2013
Cited alongside, same era.
G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning, Vol. 112, Springer, 2013
2013
Cited alongside, same era.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks (2013) · 2013
Cited alongside, same era.
B. Yu, et al., Stability, Bernoulli 19 (4) (2013) 1484–1500
2013
Cited alongside, same era.
D. Bau, B. Zhou, A. Khosla, A. Oliva, A. Torralba, Network dissection: Quantifying interpretability of deep visual representations, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6541–6549
2017
Later among the works it cites.
M. Kearns, S. Neel, A. Roth, Z. S. Wu, Preventing fairness gerrymandering: Auditing and learning for subgroup fairness (2017) · 2017
Later among the works it cites.
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, R. Sayres, Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV) (2017) · 2017
Later among the works it cites.
W. J. Murdoch, A. Szlam, Automatic rule extraction from long short term memory networks (2017) · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. W. Hosmer Jr, S. Lemeshow, R. X. Sturdivant, Applied logistic regression, Vol. 398, John Wiley & Sons, 2013
2013
Cited alongside, same era.
S. B. Imandoust, M. Bolandraftar, Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background, International Journal of Engineering Research and Applications 3 (5) (2013) 605–610
2013
Cited alongside, same era.
P. Cortez, M. J. Embrechts, Using sensitivity analysis and visualization techniques to open black box data mining models, Information Sciences 225 (2013) 1–17
2013
Cited alongside, same era.
W. Landecker, M. D. Thomure, L. M. Bettencourt, M. Mitchell, G. T. Kenyon, S. P. Brumby, Interpreting individual classifications of hierarchical networks, in: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2013, pp. 32–38
2013
Cited alongside, same era.
K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps (2013) · 2013
Cited alongside, same era.
M. Lin, Q. Chen, S. Yan, Network in network (2013) · 2013
Cited alongside, same era.
D. P. Kingma, M. Welling, Auto-Encoding Variational Bayes (2013) · 2013
Cited alongside, same era.
A. Radford, R. Jozefowicz, I. Sutskever, Learning to generate reviews and discovering sentiment (2017) · 2017
Later among the works it cites.
R. Shwartz-Ziv, N. Tishby, Opening the black box of deep neural networks via information (2017) · 2017
Later among the works it cites.
P. Gajane, M. Pechenizkiy, On formalizing fairness in prediction with machine learning (2017) · 2017
Later among the works it cites.
H. Lakkaraju, E. Kamar, R. Caruana, J. Leskovec, Interpretable & explorable approximations of black box models (2017) · 2017
Later among the works it cites.
S. Mishra, B. L. Sturm, S. Dixon, Local interpretable model-agnostic explanations for music content analysis., in: ISMIR, 2017, pp. 537–543
2017
Later among the works it cites.
O. Bastani, C. Kim, H. Bastani, Interpretability via model extraction (2017) · 2017
Later among the works it cites.
P. W. Koh, P. Liang, Understanding black-box predictions via influence functions, in: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, 2017, pp. 1885–1894
2017
Later among the works it cites.
S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems, 2017, pp. 4765–4774
2017
Later among the works it cites.
P. Dabkowski, Y. Gal, Real time image saliency for black box classifiers, in: Advances in Neural Information Processing Systems, 2017, pp. 6967–6976
2017
Later among the works it cites.
S. Krishnan, E. Wu, Palm: Machine learning explanations for iterative debugging, in: Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, ACM, 2017, p. 4
2017
Later among the works it cites.
D. Chen, S. P. Fraiberger, R. Moakler, F. Provost, Enhancing transparency and control when drawing data-driven inferences about individuals, Big data 5 (3) (2017) 197–212
2017
Later among the works it cites.
G. Tolomei, F. Silvestri, A. Haines, M. Lalmas, Interpretable predictions of tree-based ensembles via actionable feature tweaking, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2017, pp. 465–474
2017
Later among the works it cites.
A. Shrikumar, P. Greenside, A. Kundaje, Learning Important Features Through Propagating Activation Differences (2017) · 2017
Later among the works it cites.
M. Sundararajan, A. Taly, Q. Yan, Axiomatic attribution for deep networks, in: International Conference on Machine Learning, Vol. 70, JMLR. org, 2017, pp. 3319–3328
2017
Later among the works it cites.
F. Wang, M. Jiang, C. Qian, S. Yang, C. Li, H. Zhang, X. Wang, X. Tang, Residual attention network for image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3156–3164
2017
Later among the works it cites.
L. Arras, G. Montavon, K.-R. Müller, W. Samek, Explaining recurrent neural network predictions in sentiment analysis (2017) · 2017
Later among the works it cites.
J. Clos, N. Wiratunga, S. Massie, Towards explainable text classification by jointly learning lexicon and modifier terms, in: IJCAI-17 Workshop on Explainable AI (XAI), 2017, p. 19
2017
Later among the works it cites.
L. Breiman, Classification and regression trees, Routledge, 2017
2017
Later among the works it cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618–626
2017
Later among the works it cites.
doi:10.23915/distill.00007
C. Olah, A. Mordvintsev, L. Schubert, Feature visualization., DistillHttps://distill.pub/2017/feature-visualization (2017) · 2017
Later among the works it cites.
I. Donadello, L. Serafini, A. D. Garcez, Logic tensor networks for semantic image interpretation, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI (2017) 1596–1602
2017
Later among the works it cites.
D. Smilkov, N. Thorat, B. Kim, F. Viégas, M. Wattenberg, SmoothGrad: removing noise by adding noise (2017) · 2017
Later among the works it cites.
M. Ancona, E. Ceolini, C. Öztireli, M. Gross, Towards better understanding of gradient-based attribution methods for Deep Neural Networks (2017) · 2017
Later among the works it cites.
S. Du, H. Guo, A. Simpson, Self-driving car steering angle prediction based on image recognition, Tech. rep., Technical Report, Stanford University (2017)
2017
Later among the works it cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need (2017) · 2017
Later among the works it cites.
A. Slavin Ross, M. C. Hughes, F. Doshi-Velez, Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations (2017) · 2017
Later among the works it cites.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. M. Botvinick, S. Mohamed, A. Lerchner, beta-vae: Learning basic visual concepts with a constrained variational framework, in: ICLR, 2017
2017
Later among the works it cites.
S. Sabour, N. Frosst, G. E Hinton, Dynamic Routing Between Capsules (2017) · 2017
Later among the works it cites.
S. Wachter, B. Mittelstadt, L. Floridi, Why a right to explanation of automated decision-making does not exist in the general data protection regulation, International Data Privacy Law 7 (2) (2017) 76–99
2017
Later among the works it cites.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, D. Song, Robust physical-world attacks on deep learning models (2017) · 2017
Later among the works it cites.
K. R. Varshney, H. Alemzadeh, On the safety of machine learning: Cyber-physical systems, decision sciences, and data products, Big data 5 (3) (2017) 246–255
2017
Later among the works it cites.
G. Neff, A. Tanweer, B. Fiore-Gartland, L. Osburn, Critique and contribute: A practice-based framework for improving critical data studies and data science, Big data 5 (2) (2017) 85–97
2017
Later among the works it cites.
A. Karpatne, G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, V. Kumar, Theory-guided data science: A new paradigm for scientific discovery from data, IEEE Transactions on Knowledge and Data Engineering 29 (10) (2017) 2318–2331
2017
Later among the works it cites.
T. Lesort, M. Seurin, X. Li, N. Díaz-Rodríguez, D. Filliat, Unsupervised state representation learning with robotic priors: a robustness benchmark (2017) · 2017
Later among the works it cites.
J. Z. Leibo, Q. Liao, F. Anselmi, W. A. Freiwald, T. Poggio, View-tolerant face recognition and hebbian learning imply mirror-symmetric neural tuning to head orientation, Current Biology 27 (1) (2017) 62–67
2017
Later among the works it cites.
B. d’Alessandro, C. O’Neil, T. LaGatta, Conscientious classification: A data scientist’s guide to discrimination-aware classification, Big data 5 (2) (2017) 120–134
2017
Later among the works it cites.
M. Drosou, H. Jagadish, E. Pitoura, J. Stoyanovich, Diversity in big data: A review, Big data 5 (2) (2017) 73–84
2017
Later among the works it cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, B. A. y Arcas, Communication-efficient learning of deep networks from decentralized data, in: Artificial Intelligence and Statistics, 2017, pp. 1273–1282
2017
Later among the works it cites.
J. Zhao, X. Xie, X. Xu, S. Sun, Multi-view learning overview: Recent progress and new challenges, Information Fusion 38 (2017) 43–54
2017
Later among the works it cites.
D. M. West, The future of work: robots, AI, and automation, Brookings Institution Press, 2018
2018
Later among the works it cites.
Z. C. Lipton, The mythos of model interpretability, Queue 16 (3) (2018) 30:31–30:57
2018
Later among the works it cites.
A. Preece, D. Harborne, D. Braines, R. Tomsett, S. Chakraborty, Stakeholders in Explainable AI (2018) · 2018
Later among the works it cites.
J. Zhu, A. Liapis, S. Risi, R. Bidarra, G. M. Youngblood, Explainable AI for designers: A human-centered perspective on mixed-initiative co-creation, 2018 IEEE Conference on Computational Intelligence and Games (CIG) (2018) 1–8
2018
Later among the works it cites.
F. K. Dos̃ilović, M. Brc̃ić, N. Hlupić, Explainable artificial intelligence: A survey, in: 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018, pp. 210–215
2018
Later among the works it cites.
P. Hall, On the Art and Science of Machine Learning Explanations (2018) · 2018
Later among the works it cites.
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, L. Kagal, Explaining Explanations: An Overview of Interpretability of Machine Learning (2018) · 2018
Later among the works it cites.
A. Adadi, M. Berrada, Peeking inside the black-box: A survey on explainable artificial intelligence (XAI), IEEE Access 6 (2018) 52138–52160
2018
Later among the works it cites.
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi, A survey of methods for explaining black box models, ACM Computing Surveys 51 (5) (2018) 93:1–93:42
2018
Later among the works it cites.
J. Haspiel, N. Du, J. Meyerson, L. P. Robert Jr, D. Tilbury, X. J. Yang, A. K. Pradhan, Explanations and expectations: Trust building in automated vehicles, in: Companion of the ACM/IEEE International Conference on Human-Robot Interaction, ACM, 2018, pp. 119–120
2018
Later among the works it cites.
A. Chander, R. Srinivasan, S. Chelian, J. Wang, K. Uchino, Working with beliefs: AI transparency in the enterprise., in: Workshops of the ACM Conference on Intelligent User Interfaces, 2018
2018
Later among the works it cites.
O. Goudet, D. Kalainathan, P. Caillou, I. Guyon, D. Lopez-Paz, M. Sebag, Learning functional causal models with generative neural networks, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, Springer, 2018, pp. 39–80
2018
Later among the works it cites.
C. Wadsworth, F. Vera, C. Piech, Achieving fairness through adversarial learning: an application to recidivism prediction (2018) · 2018
Later among the works it cites.
F. J. C. Garcia, D. A. Robb, X. Liu, A. Laskov, P. Patron, H. Hastie, Explain yourself: A natural language interface for scrutable autonomous robots (2018) · 2018
Later among the works it cites.
G. Ras, M. van Gerven, P. Haselager, Explanation methods in deep learning: Users, values, concerns and challenges, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, Springer, 2018, pp. 19–36
2018
Later among the works it cites.
M. A. Neerincx, J. van der Waa, F. Kaptein, J. van Diggelen, Using perceptual and cognitive explanations for enhanced human-agent team performance, in: International Conference on Engineering Psychology and Cognitive Ergonomics, Springer, 2018, pp. 204–214
2018
Later among the works it cites.
M. Wu, M. C. Hughes, S. Parbhoo, M. Zazzi, V. Roth, F. Doshi-Velez, Beyond sparsity: Tree regularization of deep models for interpretability, in: AAAI Conference on Artificial Intelligence, 2018, pp. 1670–1678
2018
Later among the works it cites.
R. Goebel, A. Chander, K. Holzinger, F. Lecue, Z. Akata, S. Stumpf, P. Kieseberg, A. Holzinger, Explainable AI: the new 42?, in: International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer, 2018, pp. 295–303
2018
Later among the works it cites.
B. Green, “Fair” risk assessments: A precarious approach for criminal justice reform, in: 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning, 2018
2018
Later among the works it cites.
M. Kim, O. Reingold, G. Rothblum, Fairness through computationally-bounded awareness, in: Advances in Neural Information Processing Systems, 2018, pp. 4842–4852
2018
Later among the works it cites.
D. Linsley, D. Shiebler, S. Eberhardt, T. Serre, Global-and-local attention networks for visual recognition (2018) · 2018
Later among the works it cites.
Q. Zhang, Y. Nian Wu, S.-C. Zhu, Interpretable convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8827–8836
2018
Later among the works it cites.
K. Xu, D. H. Park, C. Yi, C. Sutton, Interpreting deep classifier by visual distillation of dark knowledge (2018) · 2018
Later among the works it cites.
S. Tan, R. Caruana, G. Hooker, Y. Lou, Distill-and-compare: Auditing black-box models using transparent model distillation, in: AAAI/ACM Conference on AI, Ethics, and Society, ACM, 2018, pp. 303–310
2018
Later among the works it cites.
M. Staniak, P. Biecek, Explanations of Model Predictions with live and breakDown Packages, The R Journal 10 (2) (2018) 395–409
2018
Later among the works it cites.
A. Polino, R. Pascanu, D. Alistarh, Model compression via distillation and quantization (2018) · 2018
Later among the works it cites.
C. Dwork, C. Ilvento, Composition of fairsystems (2018) · 2018
Later among the works it cites.
K. Burns, L. A. Hendricks, K. Saenko, T. Darrell, A. Rohrbach, Women also Snowboard: Overcoming Bias in Captioning Models (2018) · 2018
Later among the works it cites.
P. Adler, C. Falk, S. A. Friedler, T. Nix, G. Rybeck, C. Scheidegger, B. Smith, S. Venkatasubramanian, Auditing black-box models for indirect influence, Knowledge and Information Systems 54 (1) (2018) 95–122
2018
Later among the works it cites.
R. Guidotti, A. Monreale, S. Ruggieri, D. Pedreschi, F. Turini, F. Giannotti, Local rule-based explanations of black box decision systems (2018) · 2018
Later among the works it cites.
M. T. Ribeiro, S. Singh, C. Guestrin, Anchors: High-precision model-agnostic explanations, in: AAAI Conference on Artificial Intelligence, 2018, pp. 1527–1535
2018
Later among the works it cites.
G. Casalicchio, C. Molnar, B. Bischl, Visualizing the feature importance for black box models, in: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2018, pp. 655–670
2018
Later among the works it cites.
N. F. Rajani, R. Mooney, Stacking with auxiliary features for visual question answering, in: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2018, pp. 2217–2226
2018
Later among the works it cites.
N. F. Rajani, R. J. Mooney, Ensembling visual explanations, in: Explainable and Interpretable Models in Computer Vision and Machine Learning, Springer, 2018, pp. 155–172
2018
Later among the works it cites.
J. Adebayo, J. Gilmer, I. Goodfellow, B. Kim, Local explanation methods for deep neural networks lack sensitivity to parameter values (2018) · 2018
Later among the works it cites.
N. Papernot, P. McDaniel, Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning (2018) · 2018
Later among the works it cites.
S. M. Lundberg, G. G. Erion, S.-I. Lee, Consistent individualized feature attribution for tree ensembles (2018) · 2018
Later among the works it cites.
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, B. Kim, Sanity checks for saliency maps, in: Advances in Neural Information Processing Systems, 2018, pp. 9505–9515
2018
Later among the works it cites.
C. Olah, A. Satyanarayan, I. Johnson, S. Carter, L. Schubert, K. Ye, A. Mordvintsev, The building blocks of interpretability , Distill (2018). URL https://distill.pub/2018/building-blocks/
2018
Later among the works it cites.
I. Donadello, Semantic image interpretation-integration of numerical data and logical knowledge for cognitive vision, Ph.D. thesis, University of Trento (2018)
2018
Later among the works it cites.
R. Manhaeve, S. Dumancic, A. Kimmig, T. Demeester, L. De Raedt, DeepProbLog: Neural probabilistic logic programming, in: Advances in Neural Information Processing Systems 31, 2018, pp. 3749–3759
2018
Later among the works it cites.
N. Narodytska, A. Ignatiev, F. Pereira, J. Marques-Silva, Learning optimal decision trees with SAT, in: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, 2018, pp. 1362–1368
2018
Later among the works it cites.
V. Bellini, A. Schiavone, T. Di Noia, A. Ragone, E. Di Sciascio, Knowledge-aware autoencoders for explainable recommender systems, in: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, DLRS 2018, 2018, pp. 24–31
2018
Later among the works it cites.
C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, C. Hawthorne, A. M. Dai, M. D. Hoffman, D. Eck, Music transformer: Generating music with long-term structure (2018) · 2018
Later among the works it cites.
Y. Zhang, X. Chen, Explainable Recommendation: A Survey and New Perspectives (2018) · 2018
Later among the works it cites.
J. Frankle, M. Carbin, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (2018) · 2018
Later among the works it cites.
D. Huk Park, L. A. Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell, M. Rohrbach, Multimodal Explanations: Justifying Decisions and Pointing to the Evidence (2018) · 2018
Later among the works it cites.
Q. Zhang, Y. Yang, Y. Liu, Y. Nian Wu, S.-C. Zhu, Unsupervised Learning of Neural Networks to Explain Neural Networks (2018) · 2018
Later among the works it cites.
C. Rudin, Please stop explaining black box models for high stakes decisions (2018) · 2018
Later among the works it cites.
R. R. Hoffman, S. T. Mueller, G. Klein, J. Litman, Metrics for explainable ai: Challenges and prospects (2018) · 2018
Later among the works it cites.
S. Mohseni, N. Zarei, E. D. Ragan, A multidisciplinary survey and framework for design and evaluation of explainable ai systems (2018) · 2018
Later among the works it cites.
T. Orekondy, B. Schiele, M. Fritz, Knockoff nets: Stealing functionality of black-box models (2018) · 2018
Later among the works it cites.
B. Biggio, I. Pillai, S. R. Bulò, D. Ariu, M. Pelillo, F. Roli, Is data clustering in adversarial settings secure? (2018) · 2018
Later among the works it cites.
D. Charte, F. Charte, S. García, M. J. del Jesus, F. Herrera, A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines, Information Fusion 44 (2018) 78–96
2018
Later among the works it cites.
C. F. Baumgartner, L. M. Koch, K. Can Tezcan, J. Xi Ang, E. Konukoglu, Visual feature attribution using wasserstein gans, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8309–8319
2018
Later among the works it cites.
C. Biffi, O. Oktay, G. Tarroni, W. Bai, A. De Marvao, G. Doumou, M. Rajchl, R. Bedair, S. Prasad, S. Cook, et al., Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2018, pp. 464–471
2018
Later among the works it cites.
C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (2018) · 2018
Later among the works it cites.
B. C. Stahl, D. Wright, Ethics and privacy in ai and big data: Implementing responsible research and innovation, IEEE Security & Privacy 16 (3) (2018) 26–33
2018
Later among the works it cites.
T. Speicher, H. Heidari, N. Grgic-Hlaca, K. P. Gummadi, A. Singla, A. Weller, M. B. Zafar, A unified approach to quantifying algorithmic unfairness: Measuring individual group unfairness via inequality indices, in: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, ACM, 2018, pp. 2239–2248
2018
Later among the works it cites.
B. H. Zhang, B. Lemoine, M. Mitchell, Mitigating unwanted biases with adversarial learning, in: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, ACM, 2018, pp. 335–340
2018
Later among the works it cites.
J. Dressel, H. Farid, The accuracy, fairness, and limits of predicting recidivism, Science advances 4 (1) (2018) eaao5580
2018
Later among the works it cites.
S. Ramírez-Gallego, A. Fernández, S. García, M. Chen, F. Herrera, Big data: Tutorial and guidelines on information and process fusion for analytics algorithms with mapreduce, Information Fusion 42 (2018) 51–61
2018
Later among the works it cites.
Q. Sun, A. Tewari, W. Xu, M. Fritz, C. Theobalt, B. Schiele, A hybrid model for identity obfuscation by face replacement, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 553–569
2018
Later among the works it cites.
T. Miller, Explanation in artificial intelligence: Insights from the social sciences, Artif. Intell. 267 (2019) 1–38
2019
Closest in time.
S. T. Shane T. Mueller, R. R. Hoffman, W. Clancey, G. Klein, Explanation in Human-AI Systems: A Literature Meta-Review Synopsis of Key Ideas and Publications and Bibliography for Explainable AI, Tech. rep., Defense Advanced Research Projects Agency (DARPA) XAI Program (2019)
2019
Closest in time.
A. Fernandez, F. Herrera, O. Cordon, M. Jose del Jesus, F. Marcelloni, Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?, IEEE Computational Intelligence Magazine 14 (1) (2019) 69–81
2019
Closest in time.
F. Rossi, AI Ethics for Enterprise AI (2019). URL https://economics.harvard.edu/files/economics/files/rossi-francesca_4-22-19_ai-ethics-for-enterprise-ai_ec3118-hbs.pdf
2019
Closest in time.
X. Yuan, P. He, Q. Zhu, X. Li, Adversarial examples: Attacks and defenses for deep learning, IEEE Transactions on Neural Networks and Learning Systems 30 (9) (2019) 2805–2824
2019
Closest in time.
Q. Zhang, Y. Yang, H. Ma, Y. N. Wu, Interpreting CNNs via decision trees, in: IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 6261–6270
2019
Closest in time.
J. Wagner, J. M. Kohler, T. Gindele, L. Hetzel, J. T. Wiedemer, S. Behnke, Interpretable and fine-grained visual explanations for convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 9097–9107
2019
Closest in time.
A. Kanehira, T. Harada, Learning to explain with complemental examples, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 8603–8611
2019
Closest in time.
P. E. Pope, S. Kolouri, M. Rostami, C. E. Martin, H. Hoffmann, Explainability methods for graph convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 10772–10781
2019
Closest in time.
S. Barocas, M. Hardt, A. Narayanan, Fairness and Machine Learning, fairmlbook.org, 2019, http://www.fairmlbook.org
2019
Closest in time.
A. Bennetot, J.-L. Laurent, R. Chatila, N. Díaz-Rodríguez, Towards explainable neural-symbolic visual reasoning, in: NeSy Workshop IJCAI 2019, Macau, China, 2019
2019
Closest in time.
I. Donadello, M. Dragoni, C. Eccher, Persuasive explanation of reasoning inferences on dietary data, in: First Workshop on Semantic Explainability @ ISWC 2019, 2019
2019
Closest in time.
O. Loyola-González, Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view, IEEE Access 7 (2019) 154096–154113
2019
Closest in time.
K. Bollacker, N. Díaz-Rodríguez, X. Li, Extending knowledge graphs with subjective influence networks for personalized fashion, in: E. Portmann, M. E. Tabacchi, R. Seising, A. Habenstein (Eds.), Designing Cognitive Cities, Springer International Publishing, 2019, pp. 203–233
2019
Closest in time.
M. Zolotas, Y. Demiris, Towards explainable shared control using augmented reality, 2019
2019
Closest in time.
A. Diez-Olivan, J. Del Ser, D. Galar, B. Sierra, Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0, Information Fusion 50 (2019) 92–111
2019
Closest in time.
R. M. J. Byrne, Counterfactuals in explainable artificial intelligence (XAI): Evidence from human reasoning, in: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, 2019, pp. 6276–6282
2019
Closest in time.
M. Garnelo, M. Shanahan, Reconciling deep learning with symbolic artificial intelligence: representing objects and relations, Current Opinion in Behavioral Sciences 29 (2019) 17–23
2019
Closest in time.
S. J. Oh, B. Schiele, M. Fritz, Towards reverse-engineering black-box neural networks, in: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 2019, pp. 121–144
2019
Closest in time.
Z. Pan, W. Yu, X. Yi, A. Khan, F. Yuan, Y. Zheng, Recent progress on generative adversarial networks (gans): A survey, IEEE Access 7 (2019) 36322–36333
2019
Closest in time.
J. Fjeld, H. Hilligoss, N. Achten, M. L. Daniel, J. Feldman, S. Kagay, Principled artificial intelligence: A map of ethical and rights-based approaches (2019). URL https://ai-hr.cyber.harvard.edu/images/primp-viz.pdf
2019
Closest in time.
High Level Expert Group on Artificial Intelligence, Ethics guidelines for trustworthy ai, Tech. rep., European Commission (2019)
2019
Closest in time.
Y. Ahn, Y.-R. Lin, Fairsight: Visual analytics for fairness in decision making, IEEE transactions on visualization and computer graphics (2019)
2019
Closest in time.
2019
Closest in time.
U. Aivodji, H. Arai, O. Fortineau, S. Gambs, S. Hara, A. Tapp, Fairwashing: the risk of rationalization, in: International Conference on Machine Learning, 2019, pp. 161–170
2019
Closest in time.
2019
Closest in time.
P. Wang, L. T. Yang, J. Li, J. Chen, S. Hu, Data fusion in cyber-physical-social systems: State-of-the-art and perspectives, Information Fusion 51 (2019) 42–57
2019
Closest in time.
W. Ding, X. Jing, Z. Yan, L. T. Yang, A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion, Information Fusion 51 (2019) 129–144
2019
Closest in time.
A. Smirnov, T. Levashova, Knowledge fusion patterns: A survey, Information Fusion 52 (2019) 31–40
2019
Closest in time.
W. Ding, X. Jing, Z. Yan, L. T. Yang, A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion, Information Fusion 51 (2019) 129–144
2019
Closest in time.
P. Wang, L. T. Yang, J. Li, J. Chen, S. Hu, Data fusion in cyber-physical-social systems: State-of-the-art and perspectives, Information Fusion 51 (2019) 42–57
2019
Closest in time.
B. P. L. Lau, S. H. Marakkalage, Y. Zhou, N. U. Hassan, C. Yuen, M. Zhang, U.-X. Tan, A survey of data fusion in smart city applications, Information Fusion 52 (2019) 357–374
2019
Closest in time.
R. Zhang, F. Nie, X. Li, X. Wei, Feature selection with multi-view data: A survey, Information Fusion 50 (2019) 158–167
2019
Closest in time.
M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, T. Gebru, Model cards for model reporting, in: Proceedings of the Conference on Fairness, Accountability, and Transparency, ACM, 2019, pp. 220–229
2019
Closest in time.
S. Sun, A survey of multi-view machine learning, Neural computing and applications 23 (7-8) (2013) 2031–2038
2038
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, Y. Bengio, Show, attend and tell: Neural image caption generation with visual attention, in: International Conference on Machine Learning, 2015, pp. 2048–2057
2057
Closest in time.