skip to main content
10.1145/3447548.3470799acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
abstract

Machine Learning Robustness, Fairness, and their Convergence

Published: 14 August 2021 Publication History

Abstract

Responsible AI becomes critical where robustness and fairness must be satisfied together. Traditionally, the two topics have been studied by different communities for different applications. Robust training is designed for noisy or poisoned data where image data is typically considered. In comparison, fair training primarily deals with biased data where structured data is typically considered. Nevertheless, robust training and fair training are fundamentally similar in considering that both of them aim at fixing the inherent flaws of real-world data. In this tutorial, we first cover state-of-the-art robust training techniques where most of the research is on combating various label noises. In particular, we cover label noise modeling, robust training approaches, and real-world noisy data sets. Then, proceeding to the related fairness literature, we discuss pre-processing, in-processing, and post-processing unfairness mitigation techniques, depending on whether the mitigation occurs before, during, or after the model training. Finally, we cover the recent trend emerged to combine robust and fair training in two flavors: the former is to make the fair training more robust (i.e., robust fair training), and the latter is to consider robustness and fairness as two equals to incorporate them into a holistic framework. This tutorial is indeed timely and novel because the convergence of the two topics is increasingly common, but yet to be addressed in tutorials. The tutors have extensive experience publishing papers in top-tier machine learning and data mining venues and developing machine learning platforms.

References

[1]
Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In NeurIPS. 3315--3323.
[2]
Yuji Roh, Kangwook Lee, Steven Whang, and Changho Suh. 2020. FR-Train: A Mutual Information-Based Approach to Fair and Robust Training. In ICML . 8147--8157.
[3]
Hwanjun Song, Minseok Kim, and Jae-Gil Lee. 2019. SELFIE: Refurbishing Unclean Samples for Robust Deep Learning. In ICML . 5907--5915.
[4]
Hwanjun Song, Minseok Kim, Dongmin Park, and Jae-Gil Lee. 2021. Robust Learning by Self-Transition for Handling Noisy Labels. In KDD .
[5]
Suresh Venkatasubramanian. 2019. Algorithmic Fairness: Measures, Methods and Representations. In PODS. 481.
[6]
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017. Understanding Deep Learning Requires Rethinking Generalization. In ICLR .

Cited By

View all
  • (2024)Time Series Prediction Problems Under Covariate Drift2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS61622.2024.10606927(414-419)Online publication date: 17-May-2024
  • (2024)A comprehensive survey and classification of evaluation criteria for trustworthy artificial intelligenceAI and Ethics10.1007/s43681-024-00590-8Online publication date: 21-Oct-2024
  • (2024)Policy advice and best practices on bias and fairness in AIEthics and Information Technology10.1007/s10676-024-09746-w26:2Online publication date: 29-Apr-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
August 2021
4259 pages
ISBN:9781450383325
DOI:10.1145/3447548
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2021

Check for updates

Author Tags

  1. convergence
  2. fairness
  3. machine learning
  4. robustness

Qualifiers

  • Abstract

Funding Sources

  • Google AI Focused Research Award
  • Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT)

Conference

KDD '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)161
  • Downloads (Last 6 weeks)22
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Time Series Prediction Problems Under Covariate Drift2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS61622.2024.10606927(414-419)Online publication date: 17-May-2024
  • (2024)A comprehensive survey and classification of evaluation criteria for trustworthy artificial intelligenceAI and Ethics10.1007/s43681-024-00590-8Online publication date: 21-Oct-2024
  • (2024)Policy advice and best practices on bias and fairness in AIEthics and Information Technology10.1007/s10676-024-09746-w26:2Online publication date: 29-Apr-2024
  • (2024)A comprehensive review of model compression techniques in machine learningApplied Intelligence10.1007/s10489-024-05747-w54:22(11804-11844)Online publication date: 1-Nov-2024
  • (2023)Beyond generalization: a theory of robustness in machine learningSynthese10.1007/s11229-023-04334-9202:4Online publication date: 27-Sep-2023
  • (2023)Data collection and quality challenges in deep learning: a data-centric AI perspectiveThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00775-932:4(791-813)Online publication date: 3-Jan-2023
  • (2022)Multivariate time series prediction of complex systems based on graph neural networks with location embedding graph structure learningAdvanced Engineering Informatics10.1016/j.aei.2022.10181054:COnline publication date: 1-Oct-2022
  • (2021)Sample selection for fair and robust trainingProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540324(815-827)Online publication date: 6-Dec-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media