NIST AI RMF Playbook
AI RMF Playbook > Measure > MEASURE 2.11 (part 4)

Abigail Z. Jacobs and Hanna Wallach. "Measurement and Fairness." FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, March 2021, 375-85. [URL](https://doi.org/10.1145/3442188.3445901)

Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. "Quantifying and Reducing Stereotypes in Word Embeddings." arXiv preprint, submitted June 20, 2016. [URL](https://arxiv.org/abs/1606.06121)

Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. "Semantics Derived Automatically from Language Corpora Contain Human-Like Biases." Science 356, no. 6334 (April 14, 2017): 183-86. [URL](https://doi.org/10.1126/science.aal4230)

Sina Fazelpour and Maria De-Arteaga. "Diversity in Sociotechnical Machine Learning Systems." Big Data and Society 9, no. 1 (2022). [URL](https://doi.org/10.1177/20539517221082027)

Fairlearn. "Fairness in Machine Learning." Fairlearn 0.8.0 Documentation, n.d. [URL](https://fairlearn.org/v0.8/user_guide/fairness_in_machine_learning.html#)

Safiya Umoja Noble. Algorithms of Oppression: How Search Engines Reinforce Racism. New York, NY: New York University Press, 2018. [URL](http://algorithmsofoppression.com/)

Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. "Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations." Science 366, no. 6464 (October 25, 2019): 447-53. [URL](https://doi.org/10.1126/science.aax2342)

Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. "A Reductions Approach to Fair Classification." arXiv preprint, submitted July 16, 2018. [URL](https://arxiv.org/abs/1803.02453)

Moritz Hardt, Eric Price, and Nathan Srebro. "Equality of Opportunity in Supervised Learning." arXiv preprint, submitted October 7, 2016. [URL](https://arxiv.org/abs/1610.02413)

Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu. "Fair Regression: Quantitative Definitions and Reduction-Based Algorithms." Proceedings of the 36th International Conference on Machine Learning, PMLR 97:120-129, 2019. [URL](http://proceedings.mlr.press/v97/agarwal19d.html)

Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. "Fairness and Abstraction in Sociotechnical Systems." FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency, January 29, 2019, 59-68. [URL](https://doi.org/10.1145/3287560.3287598)

Matthew Kay, Cynthia Matuszek, and Sean A. Munson. "Unequal Representation and Gender Stereotypes in Image Search Results for Occupations." CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, April 18, 2015, 3819-28. [URL](https://doi.org/10.1145/2702123.2702520)

### Software Resources

- [aequitas](https://github.com/dssg/aequitas)
- AI Fairness 360:
- [Python](https://github.com/Trusted-AI/AIF360)
- [R](https://github.com/Trusted-AI/AIF360/tree/master/aif360/aif360-r)
- [algofairness](https://github.com/algofairness)
- [fairlearn](https://github.com/fairlearn/fairlearn)
- [fairml](https://github.com/adebayoj/fairml)
- [fairmodels](https://github.com/ModelOriented/fairmodels)
- [fairness](https://cran.r-project.org/web/packages/fairness/index.html)
- [solas-ai-disparity](https://github.com/SolasAI/solas-ai-disparity)
- [tensorflow/fairness-indicators](https://github.com/tensorflow/fairness-indicators)
- [Themis](https://github.com/LASER-UMASS/Themis)