Wk 10. Validation of Machine Learning Models (Bias, Fairness, and Assurance)

Lecture Date: October 23, 2023 - Monday
Lecturer: Dr. Hamdi Kavak

This week, we will discuss one of the most critical and hot topics in machine learning: Bias, Fairness, and Assurance. The lecture will clarify the terminology and feature real world examples on how machine learning can go wrong, especially for underrepresented populations. After the introductory lecture by the instructor, one student will present the paper assigned for this week (PP 5).

Slides:

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Assigned Reading (choose one for your SWA 6):

• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163.
• Adeli, E., Zhao, Q., Pfefferbaum, A., Sullivan, E. V., Fei-Fei, L., Niebles, J. C., & Pohl, K. M. (2021). Representation learning with statistical independence to mitigate bias. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2513-2523).
• Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., … & Zhang, Y. (2018). AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943.

Recommended Reading:

• Batarseh, F. A., Freeman, L., & Huang, C. H. (2021). A survey on artificial intelligence assurance. Journal of Big Data, 8(1), 1-30.
• Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (technology) is power: A critical survey of” bias” in nlp. arXiv preprint arXiv:2005.14050.
• Fuhl, W., Rong, Y., Motz, T., Scheidt, M., Hartel, A., Koch, A., & Kasneci, E. (2021, January). Explainable online validation of machine learning models for practical applications. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 3304-3311). IEEE.
• Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.
• Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1-35.
• Hagras, H. (2018). Toward human-understandable, explainable AI. Computer, 51(9), 28-36.
• Yang, K., Qinami, K., Fei-Fei, L., Deng, J., & Russakovsky, O. (2020, January). Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 547-558).