Data Science in a Mathematics Classroom: Lessons on AI Fairness
Pages 171 - 172
Abstract
Mathematics forms the foundation of computer science. By applying mathematical concepts to computer science, students can gain a deeper appreciation for the significance of mathematics and become more engaged in the learning process. This paper explores how integrating data science and AI fairness concepts into two distinct mathematics courses—Discrete Math and Algebra 1—enhances student learning and engagement.
In Discrete Math, an upper-level course focusing on discrete mathematical structures, data science applications provide concrete examples of abstract concepts. For instance, recommendation systems and their fairness can be used to motivate graph theory. Similarly, in Algebra 1, a foundational course for future mathematics study, AI fairness problems offer real-world contexts for algebraic concepts. Students can explore linear equations and inequalities by examining AI decision-making processes and their potential biases.
By incorporating these data science and AI fairness elements, we transform potentially abstract or mundane mathematical activities into meaningful, relevant learning experiences. This approach not only facilitates a deeper understanding of mathematical concepts but also introduces students to critical ethical considerations in technology.
References
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Vishnu Asutosh Dasu, Ashish Kumar, Saeid Tizpaz-Niari, and Gang Tan. 2024. NeuFair: Neural Network Fairness Repair with Dropout. The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA’24) (2024). https://doi.org/10.48550/ARXIV.2407.04268 arXiv:2407.04268
[2]
Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, and Saeid Tizpaz-Niari. 2024. Predicting Fairness of ML Software Configurations. In Proceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering, PROMISE 2024, Porto de Galinhas, Brazil, 16 July 2024, Weiyi Shang, Maxime Lamothe, and Zhiyuan Wan (Eds.). ACM, 56–65. https://doi.org/10.1145/3663533.3664040
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Surya Mattu Julia Angwin, Jeff Larson and Lauren Kirchne. 2021. Machine Bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Online.
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Verya Monjezi, Ashish Kumar, Gang Tan, Ashutosh Trivedi, and Saeid Tizpaz-Niari. 2024. Causal Graph Fuzzing for Fair ML Sofware Development. In Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, ICSE Companion 2024, Lisbon, Portugal, April 14-20, 2024. ACM, 402–403. https://doi.org/10.1145/3639478.3643530
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Verya Monjezi, Ashutosh Trivedi, Gang Tan, and Saeid Tizpaz-Niari. 2023. Information-theoretic testing and debugging of fairness defects in deep neural networks. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 1571–1582.
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ProPublica. 2021. Compas Software Ananlysis. https://github.com/propublica/compas-analysis. Online.
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Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan, and Ashutosh Trivedi. 2022. Fairness-aware configuration of machine learning libraries. In Proceedings of the 44th International Conference on Software Engineering. 909–920.
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Normen Yu, Luciana Carreon, Gang Tan, and Saeid Tizpaz-Niari. 2024. FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software. CoRR abs/2407.01423 (2024). https://doi.org/10.48550/ARXIV.2407.01423 arXiv:2407.01423
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Normen Yu, Gang Tan, and Saeid Tizpaz-Niari. 2023. FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms. CoRR abs/2307.05029 (2023). https://doi.org/10.48550/ARXIV.2307.05029 arXiv:2307.05029
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Published: 08 December 2024
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SIGITE '24: The 25th Annual Conference on Information Technology Education
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