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# Recursos


**Referencias**

1. Adadi, Amina y Mohammed Berrada. 2018. “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).” *IEEE Access 6*: 52138–52160. 

1. Ancona, Marco, Enea Ceolini, Cengiz Oztireli y Markus Gross. 2018. “Towards better understanding of gradient-based attribution methods for Deep Neural Networks.” *Proceedings of the International Conference on Learning Representations (ICLR)*. [arXiv:1711.06104](https://arxiv.org/pdf/1711.06104.pdf).

1. Dhamdhere, Kedar, Mukund Sundararajan y Qiqi Yan. 2018. “How Important Is a Neuron?” *Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML)*. [arXiv:1805.12233](https://arxiv.org/pdf/1805.12233.pdf).

1. Dua, Dheeru y Casey Graff. 2019. UCI Machine Learning Repository [[http://archive.ics.uci.edu/ml](http://archive.ics.uci.edu/ml)]. Irvine, CA: University of California, School of Information and Computer Science.

1. Kapishnikov, Andrei, Tolga Bolukbasi, Fernanda Viegas y Michael Terry. 2019. “XRAI: Better Attributions Through Regions.” *Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)*: 4948–4957. [arXiv:1906.02825](https://arxiv.org/pdf/1906.02825.pdf).

1. Kim, Been, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas y Rory Sayres. 2018. “Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).” [arXiv:1711.11279](https://arxiv.org/pdf/1711.11279.pdf).

1. Lundberg, Scott M., Gabriel G. Erion y Su-In Lee. 2019. “Consistent Individualized Feature Attribution for Tree Ensembles.” [arXiv:1802.03888](https://arxiv.org/pdf/1802.03888.pdf).

1. Lundberg, Scott M. y Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions”. *Advances in Neural Information Processing Systems (NIPS) 30*. [arXiv:1705.07874](https://arxiv.org/pdf/1705.07874.pdf).

1. Rajpurkar, Pranav, Jian Zhang, Konstantin Lopyrev y Percy Liang. 2016. “SQuAD: 100,000\$1 Questions for Machine Comprehension of Text.” [arXiv:1606.05250](https://arxiv.org/pdf/1606.05250.pdf).

1. Ribeiro, Marco T., Sameer Singh y Carlos Guestrin. 2016. "’Why Should I Trust You?’: Explaining the Predictions of Any Classifier.” *KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*: 1135–1144. [arXiv:1602.04938](https://arxiv.org/abs/1602.04938).

1. Sundararajan, Mukund, Ankur Taly y Qiqi Yan. 2017. “Axiomatic Attribution for Deep Networks.” *Proceedings of the 34th International Conference on Machine Learning 70*: 3319–3328. [arXiv:1703.01365](https://arxiv.org/pdf/1703.01365.pdf).

**External software packages**
+ [SHAP: https://github.com/slundberg/shap](https://github.com/slundberg/shap)
+ [Captum: https://captum.ai/](https://captum.ai/)

**Lecturas adicionales**
+ [Amazon SageMaker AI Clarify Model Explainability](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-explainability.html) (documentación de SageMaker AI)
+ [Repositorio Amazon SageMaker AI Clarify](https://github.com/aws/amazon-sagemaker-clarify) (GitHub)
+ Molnar, Christoph. [Interpretable machine learning. A Guide for Making Black Box Models Explainable](https://christophm.github.io/interpretable-ml-book/), 2019.