Menu

Fair and Interpretable Machine Learning

Fair and Interpretable Machine Learning

Course type:Seminar 
Time:Wednesday 16:00 – 18:00 
Location:G.-Köhler-Allee 051, SR 00 034
Organizers: Janek Thomas, Noor Awad
Web page:HisInOne

Seminar on Fair and Interpretable Machine Learning

The seminar language will be English (even if everyone is German-spoken, to practice presentation skills in English). 

*First meeting:

  • 27th April, 16:00-18:00, SR 00 034 (G.-Köhler-Allee 051).

*Regular meetings:

  • Every Wednesday, 16:00-18:00, SR 00 034 (G.-Köhler-Allee 051).

Background

The seminar focuses on interpretable machine learning in the first half of the semester and fair machine learning in the second half.

We will discuss model-agnostic tools for fairness and interpretability as well as specific algorithms. Further topics include measuring fairness, a causal perspective on fairness and how to consider fairness in AutoML.

Organization

Each week: All Students read relevant literature. Three students prepare the topic with slides and applications. Three other students are assigned as discussants. Discussants have to meet with the group presenting prior to the session, give feedback and prepare critical discussion points and open questions. 

End of the semester: Each student has to write a short paper (10 pages) about their topic. 

Requirements

We strongly recommend that you know the foundations of

  • Machine Learning
  • For some topics: Deep Learning

Main Literature

[1] Molnar, Christoph. Interpretable machine learning. Lulu. com, 2020. – https://christophm.github.io/interpretable-ml-book/

[2] Mehrabi, Ninareh, et al. “A survey on bias and fairness in machine learning.” ACM Computing Surveys (CSUR) 54.6 (2021): 1-35.

[3] Barocas, Solon, Moritz Hardt, and Arvind Narayanan. “Fairness in machine learning.” Nips tutorial 1 (2017): 2.

Schedule

DateTopicMain Ref.Further Refs.
27.04.2022Introduction, groups and topic assignments
04.05.2022
11.05.2022[1] Chapter 1-3
18.05.2022Interpretable Machine Learning Methods[1] Chapter 5Friedman, Jerome H., and Bogdan E. Popescu. “Predictive learning via rule ensembles.” The annals of applied statistics 2.3 (2008): 916-954.
Lou, Yin, et al. “Accurate intelligible models with pairwise interactions.” Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013.
Hofner, Benjamin, et al. “A framework for unbiased model selection based on boosting.” Journal of Computational and Graphical Statistics 20.4 (2011): 956-971.
25.05.2022Global Model-Agnostic Interpretability Methods[1] Chapter 8Apley, Daniel W., and Jingyu Zhu. “Visualizing the effects of predictor variables in black box supervised learning models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82.4 (2020): 1059-1086.
Wei, Pengfei, Zhenzhou Lu, and Jingwen Song. “Variable importance analysis: a comprehensive review.” Reliability Engineering & System Safety 142 (2015): 399-432.
Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. “Examples are not enough, learn to criticize! criticism for interpretability.” Advances in neural information processing systems 29 (2016).
01.06.2022Local Model-Agnostic Interpretability Methods[1] Chapter 9Goldstein, Alex, et al. “Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.” journal of Computational and Graphical Statistics 24.1 (2015): 44-65.
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. “” Why should I trust you?” Explaining the predictions of any classifier.” Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.
Karimi, Amir-Hossein, et al. “Model-agnostic counterfactual explanations for consequential decisions.” International Conference on Artificial Intelligence and Statistics. PMLR, 2020.
08.06.2022[3] Chapter 1
15.06.2022Interpretability Methods for Neural Networks[1] Chapter 10Zeiler, Matthew D., and Rob Fergus. “Visualizing and understanding convolutional networks.” European conference on computer vision. Springer, Cham, 2014.
Kim, Been, et al. “Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).” International conference on machine learning. PMLR, 2018.
Jain, Sarthak, and Byron C. Wallace. “Attention is not explanation.” arXiv preprint arXiv:1902.10186 (2019).
22.06.2022Multi-Objective and constrained Optimization and Model SelectionTBDDeb, Kalyanmoy, et al. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE transactions on evolutionary computation 6.2 (2002): 182-197.
Knowles, Joshua. “ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems.” IEEE Transactions on Evolutionary Computation 10.1 (2006): 50-66.
Gardner, Jacob R., et al. “Bayesian Optimization with Inequality Constraints.” ICML. Vol. 2014. 2014.
29.06.2022Measures for Fairness[2] Section 4.1Hardt, Moritz, Eric Price, and Nati Srebro. “Equality of opportunity in supervised learning.” Advances in neural information processing systems 29 (2016).
Kearns, Michael, et al. “An empirical study of rich subgroup fairness for machine learning.” Proceedings of the conference on fairness, accountability, and transparency. 2019.
Chouldechova, Alexandra. “Fair prediction with disparate impact: A study of bias in recidivism prediction instruments.” Big data 5.2 (2017): 153-163.
06.07.2022Debiasing Methods[2] Section 5.1Mehrabi, Ninareh, et al. “Debiasing community detection: The importance of lowly connected nodes.” 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2019.
Calmon, Flavio, et al. “Optimized pre-processing for discrimination prevention.” Advances in neural information processing systems 30 (2017).
Kamiran, Faisal, and Toon Calders. “Data preprocessing techniques for classification without discrimination.” Knowledge and information systems 33.1 (2012): 1-33.
13.07.2022Fair Machine Learning Algorithms[2] Section 5.2Zafar, Muhammad Bilal, et al. “Fairness constraints: Mechanisms for fair classification.” Artificial Intelligence and Statistics. PMLR, 2017.
Kamishima, Toshihiro, et al. “Fairness-aware classifier with prejudice remover regularizer.” Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, 2012.
Berk, Richard, et al. “A convex framework for fair regression.” arXiv preprint arXiv:1706.02409 (2017).
20.07.2022Causal Perspective on Fair ML[3] Chapter 4Zhang, Lu, Yongkai Wu, and Xintao Wu. “A causal framework for discovering and removing direct and indirect discrimination.” arXiv preprint arXiv:1611.07509 (2016).
Loftus, Joshua R., et al. “Causal reasoning for algorithmic fairness.” arXiv preprint arXiv:1805.05859 (2018).
Nabi, Razieh, and Ilya Shpitser. “Fair inference on outcomes.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 32. No. 1. 2018.
27.07.2022Fairness & Interpretability in Model SelectionWu, Qingyun, and Chi Wang. “Fair AutoML.” arXiv preprint arXiv:2111.06495 (2021).
Cruz, André F., et al. “Promoting Fairness through Hyperparameter Optimization.” 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021.
Perrone, Valerio, et al. “Fair bayesian optimization.” Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 2021.

Grading

  • Presentation: 40%
  • Paper: 40%
  • Role as discutant: 20%

Further information

For questions, please send an email to one of the organizers: Janek Thomas