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The evergrowing reliance of humans and society on machine learning methods has raised concerns about their trustworthiness and liability. As a response to these concerns, Socially Responsible Machine Learning (SRML) aims at developing fair, transparent, and robust machine learning algorithms. However, traditional approaches to SRML do not incorporate human perspectives, and therefore are not sufficient to build long-lasting trust between machines and human being. Causality as the key to human intelligence plays a vital role in achieving socially responsible machine learning algorithms which are compatible with human notions.
Bridging the gap between traditional SRML and causality, in this tutorial, we aim at providing a holistic overview of SRML through the lens of causality. In particular, we will focus on state-of-the-art techniques on causal socially responsible ML in terms of fairness, interpretability, and robustness. The objectives of this tutorial are as follows: (1) we provide a taxonomy of existing literature on causal socially responsible ML from fairness, interpretability, and robustness perspective; (2) we review the state-of-the-art techniques for each task; and (3) we elucidate open questions and future research directions. We believe this tutorial is beneficial to researchers and practitioners from the areas of data mining, machine learning, and social sciences.
Presenters
Raha Moraffah
Arizona State University
Amir-Hossein Karimi
Max Planck Institute for Intelligent Systems
Adrienne Raglin
Army Research Lab
Huan Liu
Arizona State University
Contributors
Miriam Rateike
Saarland University
Ayan Majumdar
Max Planck Institute for Software Systems
Isabel Valera
Max Planck Institute for Software Systems, Saarland University
Tutorial Outline
(1) Background and Overview (10 mins) (Slides)
(i) Overview of Causal Socially Responsible ML
(ii) Introduction to Causality
(2) Causal Interpretability (50 mins) (Slides)
(i) Causal Data-base Interpretability
(ii) Causal Model-based Interpretability
(iii) Causal Decision-base Interpretability
(3) Causal Fairness (50 mins) (Slides)
(i) Fundamental of Causal Fairness
(ii) Causal Fairness Notions
(iii) Causal Fairness Methods
(3) Causal Robustness (50 mins) (Slides)
(i) Causal Data Augmentation
(ii) Causal Representation Learning
(iii) Causal Mechanism Learning
(iv) Post-training Methods
(4) Conclusion and Future Trends (10 mins) (Slides)
(i) Future Directions and Promises
(ii) Challenges and Limitations
References
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[16] Judea Pearl. 2009. Causality. Cambridge university press.
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[18] Numair Sani, Daniel Malinsky, and Ilya Shpitser. 2020. Explaining the behavior of black-box prediction algorithms with causal learning. arXiv:2006.02482 (2020).
[19] Patrick Schwab and Walter Karlen. 2019. Cxplain: Causal explanations for model interpretation under uncertainty. NeurIPS (2019).
[20] Paras Sheth, Raha Moraffah, K Selçuk Candan, Adrienne Raglin, and Huan Liu. 2022. Domain Generalization–A Causal Perspective. arXiv:2209.15177 (2022).
[21] David S Watson, Limor Gultchin, Ankur Taly, and Luciano Floridi. 2021. Local explanations via necessity and sufficiency: Unifying theory and practice. In UAI.
[22] Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, and Xianzhi Wang. 2020. Causality learning: a new perspective for interpretable machine learning. arXiv preprint arXiv:2006.16789 (2020).
[23] Lu Zhang, Yongkai Wu, and Xintao Wu. 2016. A causal framework for discovering and removing direct and indirect discrimination. arXiv:1611.07509 (2016).
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