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Constraining deep representations with a noise module for fair classification

Authors:

Mattia Cerrato,

Roberto Esposito,

Laura Li PumaAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 470 - 472

https://doi.org/10.1145/3341105.3374090

Published: 30 March 2020 Publication History

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Abstract

The recent surge in interest for Deep Learning (motivated by its exceptional performances on longstanding problems) made Neural Networks a very appealing tool for many actors in our society. One issue in this shift of interest is that Neural Networks are very opaque objects and it is often hard to make sense of their predictions.

In this context, research efforts have focused on building fair representations of data which display little to no correlation with regard to a sensitive attribute s. In this paper we build onto a domain adaptation neural model by augmenting it with a "noise conditioning" mechanism which we show is instrumental in obtaining fair (i.e. non-correlated with s) representations. We provide experiments on standard datasets showing the effectiveness of the noise conditioning mechanism in helping the networks to ignore the sensible attribute.

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Cited By

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Pagano TLoureiro RLisboa FPeixoto RGuimarães GCruz GAraujo MSantos LCruz MOliveira EWinkler INascimento E(2023)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation MethodsBig Data and Cognitive Computing10.3390/bdcc70100157:1(15)Online publication date: 13-Jan-2023
https://doi.org/10.3390/bdcc7010015
Cerrato MKoppel MSegner AEsposito RKramer S(2020)Fair pairwise learning to rank2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA49011.2020.00083(729-738)Online publication date: Oct-2020
https://doi.org/10.1109/DSAA49011.2020.00083

Index Terms

Constraining deep representations with a noise module for fair classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

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.

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Association for Computing Machinery

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Published: 30 March 2020

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Pagano TLoureiro RLisboa FPeixoto RGuimarães GCruz GAraujo MSantos LCruz MOliveira EWinkler INascimento E(2023)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation MethodsBig Data and Cognitive Computing10.3390/bdcc70100157:1(15)Online publication date: 13-Jan-2023
https://doi.org/10.3390/bdcc7010015
Cerrato MKoppel MSegner AEsposito RKramer S(2020)Fair pairwise learning to rank2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA49011.2020.00083(729-738)Online publication date: Oct-2020
https://doi.org/10.1109/DSAA49011.2020.00083

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Environmental Noise Source Classification Using Neural Networks

Noise Suppression by Artificial Neural Networks

Understanding and mitigating noise in trained deep neural networks

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