• Corpus ID: 24990444

Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

@article{Beutel2017DataDA,
  title={Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations},
  author={Alex Beutel and Jilin Chen and Zhe Zhao and Ed H. Chi},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.00075}
}
How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group. [] Key MethodHere, we use an adversarial training procedure to remove information about the sensitive attribute from the latent representation learned by a neural network. In particular, we study how the choice of data for the adversarial training effects the resulting fairness properties. We find two interesting results: a small amount of data is…
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References

SHOWING 1-10 OF 11 REFERENCES

Learning Fair Representations

We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the…

Equality of Opportunity in Supervised Learning

This work proposes a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features and shows how to optimally adjust any learned predictor so as to remove discrimination according to this definition.

Domain-Adversarial Training of Neural Networks

A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer.

Beyond Globally Optimal: Focused Learning for Improved Recommendations

This work offers a new technique called focused learning, based on hyperparameter optimization and a customized matrix factorization objective, which demonstrates prediction accuracy improvements on multiple datasets.

The Variational Fair Autoencoder

This model is based on a variational autoencoding architecture with priors that encourage independence between sensitive and latent factors of variation that is more effective than previous work in removing unwanted sources of variation while maintaining informative latent representations.

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

This work empirically demonstrates that its algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks.

Domain Separation Networks

The novel architecture results in a model that outperforms the state-of-the-art on a range of unsupervised domain adaptation scenarios and additionally produces visualizations of the private and shared representations enabling interpretation of the domain adaptation process.

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.

Inherent Trade-Offs in the Fair Determination of Risk Scores

Some of the ways in which key notions of fairness are incompatible with each other are suggested, and hence a framework for thinking about the trade-offs between them is provided.

Censoring Representations with an Adversary

This work forms the adversarial model as a minimax problem, and optimize that minimax objective using a stochastic gradient alternate min-max optimizer, and demonstrates the ability to provide discriminant free representations for standard test problems, and compares with previous state of the art methods for fairness.