NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 2.9 (part 2)

Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, et al. "Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities, and Challenges Toward Responsible AI." Information Fusion 58 (June 2020): 82-115. [URL](https://doi.org/10.1016/j.inffus.2019.12.012)

Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, and Elena L. Glassman. "Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems." IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces, March 17, 2020, 454-64. [URL](https://doi.org/10.1145/3377325.3377498)

P. Jonathon Phillips, Carina A. Hahn, Peter C. Fontana, Amy N. Yates, Kristen Greene, David A. Broniatowski, and Mark A. Przybocki. "NISTIR 8312 Four Principles of Explainable Artificial Intelligence." National Institute of Standards and Technology (NIST), September 2021. [URL](https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8312.pdf)

Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. "Model Cards for Model Reporting." FAT *19: Proceedings of the Conference on Fairness, Accountability, and Transparency, January 2019, 220-29. [URL](https://doi.org/10.1145/3287560.3287596)

Ke Yang, Julia Stoyanovich, Abolfazl Asudeh, Bill Howe, HV Jagadish, and Gerome Miklau. "A Nutritional Label for Rankings." SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data, May 27, 2018, 1773-76. [URL](https://doi.org/10.1145/3183713.3193568)

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "'Why Should I Trust You?': Explaining the Predictions of Any Classifier." arXiv preprint, submitted August 9, 2016. [URL](https://arxiv.org/abs/1602.04938)

Scott M. Lundberg and Su-In Lee. "A unified approach to interpreting model predictions." NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, December 4, 2017, 4768-4777. [URL](https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf)

Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju. "Fooling LIME and SHAP: Adversarial Attacks on Post Hoc Explanation Methods." AIES '20: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, February 7, 2020, 180-86. [URL](https://doi.org/10.1145/3375627.3375830)

David Alvarez-Melis and Tommi S. Jaakkola. "Towards robust interpretability with self-explaining neural networks." NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems, December 3, 2018, 7786-7795. [URL](https://dl.acm.org/doi/10.5555/3327757.3327875)

FinRegLab, Laura Biattner, and Jann Spiess. "Machine Learning Explainability & Fairness: Insights from Consumer Lending." FinRegLab, April 2022. [URL](https://finreglab.org/ai-machine-learning/explainability-and-fairness-of-machine-learning-in-credit-underwriting/machine-learning-explainability-fairness-insights-from-consumer-lending/)

Miguel Ferreira, Muhammad Bilal Zafar, and Krishna P. Gummadi. "The Case for Temporal Transparency: Detecting Policy Change Events in Black-Box Decision Making Systems." arXiv preprint, submitted October 31, 2016. [URL](https://arxiv.org/abs/1610.10064)

Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec. "Interpretable & Explorable Approximations of Black Box Models." arXiv preprint, July 4, 2017. [URL](https://arxiv.org/abs/1707.01154)

### Software Resources

- [SHAP](https://github.com/slundberg/shap)
- [LIME](https://github.com/marcotcr/lime)
- [Interpret](https://github.com/interpretml/interpret)
- [PiML](https://github.com/SelfExplainML/PiML-Toolbox)
- [Iml](https://cran.r-project.org/web/packages/iml/index.html)
- [Dalex](https://cran.r-project.org/web/packages/DALEX/index.html)