Space: Cyber space
Time: Slot 3, IITB time table.
Monday 10:35 to 11:30: Tuesdays 11:35 to 12:30, Thursdays : 8:30 to 9:25
Mailing List:
https://groups.google.com/g/iitb-cs689-july2021
Teaching Assistants: TBA
Contact: hariguru@cse.iitm.ac.in
Paper presentations : 2*30=60
Project : 30
Mini-quizzes : 10
Notions of fairness
Constrained classification
Algorithmic vs data unfairness
Visualizations of neural nets
Interpretable Machine Learning
Privacy (differential privacy, information theory)
Distributed/federated learning
Ethics in AI
Fairness, Accountability and Transparency in ML : https://www.fatml.org/resources/relevant-scholarship
Fairness and ML textbook : https://fairmlbook.org/
Sample Explainability Papers: http://aix360.mybluemix.net/resources
Interpretable ML textbook : https://christophm.github.io/interpretable-ml-book/
Week 0 and Week 1:
How big data is unfair: https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de
Tutorial video: https://fairmlbook.org/tutorial1.html (Slides: https://mrtz.org/nips17/#/)
Classification chapter: https://fairmlbook.org/classification.html
Demo : https://research.google.com/bigpicture/attacking-discrimination-in-ml/
Week 2 to 7:
Preprocessing methods for fairness :
Zemel et al. 2013. Learning fair representations [Class/Project]
Postprocessing methods for fairness :
Hardt et al.2016. Equality of odds and opportunity [Reading/Project]
Training for fairness :
Agarwal et al. 2018. Reductions approach to fair classification [Reading/Project]
Zafar et al. 2017. Fairness constraints [Reading/Project]
Narasimhan et al. 2020a: Split-Frank-Wolfe. [Class/Project]
Narasimhan et al. 2020b: Pairwise fairness [Reading/Project]
Individual fairness :
Kearns et al. 2019. Individual fairness, Video [Class/Reading/Project]
Dwork et al. 2012. Fairness through awareness. [Reading/Project]
Hashimoto et al 2018. Fairness without deomgraphics. [Reading/Project]
Other fairness topics:
Kearns et al. 2018: Fairness Gerrymandering [Reading/Project]
Hiranandani et al. 2020 : Fair performance metric elicitation [Reading/Project]
Joseph et al. 2016: Fairness in Bandits. [Reading/Project]
Corbett et al. 2016 : Cost of fairness. [Reading/Project]
Zhang et al. 2016. Causal Framework for Removing Discrimination. [Reading]
Chiappa et al 2019. Causal Bayesian Networks. [Class]
Kusner et al. 2017: Counterfactual Fairness. [Class]
Popular science articles:
General texts on Fairness and Discrimination:
Kleinberg et al. Discrimination in the age of algorithms.
Kleinberg et al. Algorithms as discrimination detectors.
Kleinberg et al. Algorithmic Fairness
Week 8 to 10:
Zeiler and Fergus. 2013. Occlusion Map and Deconv layers. Visualizing and Understanding Convolutional Networks. https://arxiv.org/pdf/1311.2901
Simonyan et al. 2014. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. https://arxiv.org/abs/1312.6034
Zhou et al. 2015. Learning Deep Features for Discriminative Localization. https://arxiv.org/abs/1512.04150
Selvaraju et al. 2016. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. https://arxiv.org/abs/1610.02391
Desai et al. 2020. Ablation CAM. Link
Mahendran et al. 2015. Understanding Deep Image Representations by Inverting Them. https://openaccess.thecvf.com/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf
Feature Visualisation : https://distill.pub/2017/feature-visualization/
Building blocks of interpretability: https://distill.pub/2018/building-blocks/
Interpretability and Explainability in AI: A Survey. https://arxiv.org/abs/1910.10045
Pruthi et al. 2018. Sparse Interpretable Neural Models. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17433/16024
Ancona et al. 2018. Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Pruthi et al. 2020. Evaluating Explanations: How much do explanations from the teacher aid students? https://arxiv.org/abs/2012.00893
Shanmugam et al. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. https://arxiv.org/pdf/1802.07623.pdf
Doshi Velez et al. 2017. Towards Interpretable Machine Learning. https://arxiv.org/pdf/1702.08608.pdf