# Adversarial Learned Fair Representations using Dampening and Stacking

arXiv: https://arxiv.org/abs/2203.08637 · HTML: https://mlsanity.com/paper/2203.08637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [The Variational Fair Autoencoder](https://mlsanity.com/paper/1511.00830/) (2015)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Learning Fair Representations via an Adversarial Framework](https://mlsanity.com/paper/1904.13341/) (2019)
- [Stabilizing Adversarial Nets With Prediction Methods](https://mlsanity.com/paper/1705.07364/) (2017)
- [Adversarial Stacked Auto-Encoders for Fair Representation Learning](https://mlsanity.com/paper/2107.12826/) (2021)
- [Using Adversarial Debiasing to Remove Bias from Word Embeddings](https://mlsanity.com/paper/2107.10251/) (2021)

## Built on by (most cited first, 1 of 1)

- [Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification](https://mlsanity.com/paper/2204.10467/) (2022)

## Abstract (arXiv)

As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data in which a sensitive variable is censored. Recent work aims to learn fair representations through adversarial learning. This paper builds upon this work by introducing a novel algorithm which uses dampening and stacking to learn adversarial fair representations. Results show that that our algorithm improves upon earlier work in both censoring and reconstruction.