| Date | Topic | Reading | Notes |
|---|---|---|---|
| Monday, January 13, 2025 | Course Introduction | ||
| Wednesday January 15, 2025 | Motivation -- Potential Harms of Data-driven Systems | O'neil, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown, 2017.
A presentation by the author A crash course on Utilitarianism A. Asudeh. "Enabling Responsible Data Science in Practice". In ACM SIGMOD Blog, 2021. |
π Extended Lecture Notes (Marzieh Hosseini, Nastaran Darabi) |
| Monday, January 20, 2025 | Martin Luther King Jr. Day -- No Classes | ||
| Wednesday, January 22, 2025 | Throught the lens of Fairness: An overview of Data Analytics System | Barocas, Solon, and Andrew D. Selbst. "Big data's disparate impact." Calif. L. Rev. 104 (2016): 671. |
π Extended Lecture Notes (Sina Tayebati, Yashith Reddy Enamala)
π Class Notes |
| Monday, January 27, 2025 | Throught the lens of Fairness (continued);
Fairness Definitions: Individual Fairness |
Dwork, Cynthia, et al. "Fairness through awareness." Proceedings of the 3rd innovations in theoretical computer science conference. 2012. | π Extended Lecture Notes (Ranit Debnath Akash, Mohammed Abdul Hadi) |
| Wednesday, January 29, 2025 | Fairness Definitions: Group Fairness | Barocas, Solon, Moritz Hardt, and Arvind Narayanan. ``Fairness in machine learning. Limitations and Opportunities''. ISBN 9780262048613, December 19, 2023. The MIT Press (Free Online: https://fairmlbook.org/). | π Extended Lecture Notes (Adithya Reddy Chidirala, Hemanth Srinivas Reddy Chennur) |
| Monday, February 3, 2025 | Fairness Definitions: Definition Categories; Impossibility Theorems | https://fairmlbook.org/classification.html |
π Extended Lecture Notes (Sanjna Chippalaturthi, Asritha Pidikiti)
π Class Notes (Fairness Definitions) |
| Wednesday, February 5, 2025 | Impossibility Theorems (continued)
Measuring Unfairness |
Additional References for Fairness in Machine Learning:
[1] Mehrabi, Ninareh, et al. "A survey on bias and fairness in machine learning." ACM computing surveys (CSUR) 54.6 (2021): 1-35. [2] Caton, Simon, and Christian Haas. "Fairness in machine learning: A survey." ACM Computing Surveys 56.7 (2024): 1-38. [3] Pessach, Dana, and Erez Shmueli. "A review on fairness in machine learning." ACM Computing Surveys (CSUR) 55.3 (2022): 1-44. |
Project Proposal Deadline π Extended Lecture Notes (Nandini Satyanarayan Jirobe, Parikha Goyanka) |
| Monday, February 10, 2025 | Fair ML: Preprocess interventions |
[1] Kamiran, Faisal, and Toon Calders. "Data preprocessing techniques for classification without discrimination." Knowledge and information systems 33.1 (2012): 1-33.
[2] Calmon, Flavio, et al. "Optimized pre-processing for discrimination prevention." Advances in neural information processing systems 30 (2017). [3] Feldman, Michael, et al. "Certifying and removing disparate impact." proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 2015. |
Programming Assignment 1 posted
π Extended Lecture Notes (Aadit Shaivalbhai Trivedi, Nihal Niraj Mishra) π Class Notes (Preprocess Interventions) |
| Wednesday, February 12, 2025 | Fair ML: Inprocess Interventions; |
[1] Zafar, Muhammad Bilal, et al. "Fairness constraints: Mechanisms for fair classification." Artificial intelligence and statistics. PMLR, 2017.
[2] Agarwal, Alekh, et al. "A reductions approach to fair classification." International conference on machine learning. PMLR, 2018. |
π Extended Lecture Notes (Srinath Bellamkonda, Murali Krishna Prodduturi) |
| Monday, February 17, 2025 |
Fair ML: Price of Fairness; Pareto-Optimality in Fairness
Causalality: a double-edged sword |
[1] https://fairmlbook.org/pdf/causal.pdf
[2] Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H. (2020). A survey of learning causality with data: Problems and methods. ACM Computing Surveys (CSUR), 53(4), 1-37. |
Programming Assignment 1 Deadline
π Extended Lecture Notes (Aditya Acharya, Sanmitha Shetty) π Class Notes (InProcess Interventions;PoF;Pareto-Optimality) |
| Wednesday, February 19, 2025 | Causalality (continued) | π Extended Lecture Notes (Dakshitha Mandhalapu, Rohit Reddy Kesireddy) | |
| Monday, February 24, 2025 |
Causalality (continued);
Fair Ranking -- Part I: Score-based Ranking |
[1] Zehlike, Meike, Ke Yang, and Julia Stoyanovich. "Fairness in ranking, part i: Score-based ranking." ACM Computing Surveys 55.6 (2022): 1-36. [2] Pitoura, Evaggelia, Kostas Stefanidis, and Georgia Koutrika. "Fairness in rankings and recommendations: an overview." The VLDB Journal (2022): 1-28. |
π Extended Lecture Notes (Madhura Dongare, Resham Patil)
π Class Notes (Causality) |
| Wednesday, February 26, 2025 | Fair Ranking -- Part II: Learned Ranking | Zehlike, Meike, Ke Yang, and Julia Stoyanovich. "Fairness in ranking, part ii: Learning-to-rank and recommender systems." ACM Computing Surveys 55.6 (2022): 1-41. |
π Extended Lecture Notes (Kodati Shruthi, Anand Meena)
π Class Notes (Fair Ranking) |
| Monday, March 3, 2025 | Bias in Data: Different Sources&Types of Bias; Representation Bias |
[1] Mehrabi, Ninareh, et al. "A survey on bias and fairness in machine learning." ACM computing surveys (CSUR) 54.6 (2021): 1-35.
[2] Shahbazi, Nima, et al. "Representation bias in data: A survey on identification and resolution techniques." ACM Computing Surveys 55.13s (2023): 1-39. |
Programming Assignment 2 posted π Extended Lecture Notes (Dhiraj Shelke, Shreyash Kadam) π Class Notes (Bias in Data) |
| Wednesday, March 5, 2025 | Data Curation: Data Integration; Query Rewriting; Entity Matching |
[1] Nargesian, Fatemeh, Abolfazl Asudeh, and H. V. Jagadish. "Tailoring data source distributions for fairness-aware data integration." Proceedings of the VLDB Endowment 14.11 (2021): 2519-2532.
[3] Shahbazi, Nima, et al. "Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching." Proceedings of the VLDB Endowment 16.11 (2023): 3279-3292. [4] Shahbazi, Nima, et al. "FairEM360: A Suite for Responsible Entity Matching." Proceedings of the VLDB Endowment (2024) -- Demo Track. |
π Extended Lecture Notes (Niket Pathak, Purva Tandel)
π Class Notes (Fair Data Integration) |
| Monday, March 10, 2025 | Data Curation: Fair Range Queries - Minority mining |
[1] Shetiya, Suraj, et al. "Fairness-aware range queries for selecting unbiased data." In ICDE, 2022.
[2] Dehghankar, Mohsen, and Abolfazl Asudeh. "Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups." Proceedings of the VLDB Endowment (2025). |
Programming Assignment 2 Deadline π Class Notes (FairRangeQueries-MinorityMining) -- FairRangeQueries [Slides] π Extended Lecture Notes (Apoorv Lodhi, Niyati Malik) |
| Wednesday, March 12, 2025 | GenAI for Data Curation: Chameleon; Entity Matching | Erfanian, Mahdi, H. V. Jagadish, and Abolfazl Asudeh. "Chameleon: Foundation models for fairness-aware multi-modal data augmentation to enhance coverage of minorities." Proceedings of the VLDB Endowment (2024). | Chameleon [Slides] |
| Monday, March 17, 2025 | GenAI&Responsibility: Overview | [1] Li, Yingji, et al. "A survey on fairness in large language models." arXiv preprint arXiv:2308.10149 (2023).
[2] Gallegos, Isabel O., et al. "Bias and fairness in large language models: A survey." Computational Linguistics (2024): 1-79. |
π Extended Lecture Notes (Mohammed Muneeb, Taabish Sutriwala) |
| Wednesday, March 19, 2025 | GenAI&Responsibility: Fairness Interventions | Gallegos, Isabel O., et al. "Bias and fairness in large language models: A survey." Computational Linguistics (2024): 1-79. | π Class Notes (Bias in LLMs) |
| Monday, March 24, 2025 | Spring Break -- No Classes | ||
| Wednesday, March 26, 2025 | Spring Break -- No Classes | ||
| Monday, March 31, 2025 | GenAI&Responsibility: Requal-LM; AXOLOTL | [1] Ebrahimi, Sana, et al. "AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs." In IEEE ICKG (2024).
[2] Ebrahimi, Sana, Nima Shahbazi, and Abolfazl Asudeh. "REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models." In NAACL (2024). |
REQUAL-LM Slides AXOLOTL Slides |
| Wednesday, April 2, 2025 | GenAI&Responsibility: Input Reranking | [1] Dehghankar, Mohsen, and Abolfazl Asudeh. "Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks." arXiv preprint arXiv:2412.00546 (2024). | π Extended Lecture Notes (Harsha Konduru, Yashwanth Reddy) |
| Monday, April 7, 2025 | Masking through Cherry-picked Data Presentation: Ranking; Trendlines; News Ordering | Asudeh, Abolfazl, et al. "Perturbation-based Detection and Resolution of Cherry-picking." A Quarterly bulletin of the Computer Society of the IEEE Technical Committee on Data Engineering 45.3 (2021). |
Cherry-picking Slides π Extended Lecture Notes (Devendra Seelamneni, Aryan Rao Neelagiri) |
| Wednesday, April 9, 2025 | Fairness in Algorithm Design: Clustering | [1] Chhabra, Anshuman, Karina MasalkovaitΔ, and Prasant Mohapatra. "An overview of fairness in clustering." IEEE Access 9 (2021): 130698-130720. [2] Mehrdad Ghadiri, Samira Samadi, and Santosh Vempala. 2021. Socially Fair k-Means Clustering. In FAccT, 2021 |
π Extended Lecture Notes (Nishanth Nagendran, Aryan Esmailpour)
π Class Notes (Fair Clustering) |
| Monday, April 14, 2025 | Fairness in Algorithm Design: Set Cover; Max Cover | [1] Dehghankar, Mohsen, et al. "Fair Set Cover." In KDD 2025. [2] Asudeh, Abolfazl, et al. "Maximizing coverage while ensuring fairness: A tale of conflicting objectives." Algorithmica 85.5 (2023): 1287-1331. |
|
| Wednesday, April 16, 2025 | Fairness-aware Data Structures: Fairhash | Shahbazi, Nima, Stavros Sintos, and Abolfazl Asudeh. "FairHash: A Fair and Memory/Time-efficient Hashmap." Proceedings of the ACM on Management of Data 2.3 (2024): 1-29. [2] Aumuller, Martin, et al. "Fair near neighbor search via sampling." ACM SIGMOD Record 50.1 (2021): 42-49. |
FairHash Slides |
| Monday, April 21, 2025 | Project Presentation | ||
| Wednesday, April 23, 2025 | Project Presentation | ||
| Monday, April 28, 2025 | Project Presentation | ||
| Wednesday, April 30, 2025 | Project Presentation |