, Regulating fintech lending, Banking & Financial Services Policy Report, vol.37, 2018.
, European union regulations on algorithmic decision-making and a "right to explanation, stat, vol.1050, p.31, 2016.
DOI : 10.1609/aimag.v38i3.2741
URL : http://arxiv.org/pdf/1606.08813
, why should I trust you?": Explaining the predictions of any classifier, 2016.
, Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, 2017.
DOI : 10.2139/ssrn.3063289
URL : http://arxiv.org/pdf/1711.00399
, Storage of features, conjunctions, and objects in visual working memory, Journal of Experimental Psychology: Human Perception and Performance, vol.27, issue.1, p.92, 2001.
, The capacity of visual short-term memory is set both by visual information load and by number of objects, Psychological science, vol.15, issue.2, pp.106-111, 2004.
, An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, vol.51, pp.141-154, 2011.
, A survey of methods for explaining black box models, 2018.
, Interpretable Machine Learning, 2018.
, Extracting tree-structured representations of trained networks, Advances in Neural Information Processing Systems 8, NIPS, pp.24-30, 1995.
, Model-agnostic interpretability of machine learning. CoRR, abs/1606.05386, 2016.
, A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems, vol.30, pp.4765-4774, 2017.
, Prototype selection for interpretable classification, The Annals of Applied Statistics, pp.2403-2424, 2011.
DOI : 10.1214/11-aoas495
URL : https://doi.org/10.1214/11-aoas495
, Examples are not enough, learn to criticize! criticism for interpretability, Advances in Neural Information Processing Systems, pp.2280-2288, 2016.
, Adversarial examples in the physical world, 2016.
, Credit scoring and its applications, vol.2, 2017.
DOI : 10.1137/1.9780898718317
, Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision support systems, vol.37, pp.543-558, 2004.
DOI : 10.1016/s0167-9236(03)00086-1
, Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, vol.21, pp.117-134, 2016.
DOI : 10.1016/j.sorms.2016.10.001
URL : http://arxiv.org/pdf/1602.02137
, Consumer credit-risk models via machinelearning algorithms, Journal of Banking & Finance, vol.34, pp.2767-2787, 2010.
, Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal, Expert Systems with Applications, vol.42, issue.13, pp.5737-5753, 2015.
, Comprehensible credit scoring models using rule extraction from support vector machines, European journal of operational research, vol.183, issue.3, pp.1466-1476, 2007.
, Demonstrating non-inferiority of easy interpretable methods for insolvency prediction, Expert Systems with Applications, vol.42, issue.23, pp.9117-9128, 2015.
, Interpretable multiclass models for corporate credit rating capable of expressing doubt, Frontiers in Applied Mathematics and Statistics, vol.2, p.16, 2016.
, Incorporating sequential information in bankruptcy prediction with predictors based on markov for discrimination. Decision Support Systems, vol.98, pp.59-68, 2017.
, An improved credit card users default prediction model based on ripper, 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp.1785-1789, 2017.
, Selection of support vector machines based classifiers for credit risk domain, An International Journal, vol.42, issue.6, pp.3194-3204, 2015.
, Credit risk analysis using machine and deep learning models, Risks, vol.6, issue.2, p.38, 2018.