Differentially Private and Fair Deep Learning: A Lagrangian Dual
Approach
- URL: http://arxiv.org/abs/2009.12562v1
- Date: Sat, 26 Sep 2020 10:50:33 GMT
- Title: Differentially Private and Fair Deep Learning: A Lagrangian Dual
Approach
- Authors: Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck
- Abstract summary: This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors.
The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
- Score: 54.32266555843765
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A critical concern in data-driven decision making is to build models whose
outcomes do not discriminate against some demographic groups, including gender,
ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of
the sensitive attributes is essential, while, in practice, these attributes may
not be available due to legal and ethical requirements. To address this
challenge, this paper studies a model that protects the privacy of the
individuals sensitive information while also allowing it to learn
non-discriminatory predictors. The method relies on the notion of differential
privacy and the use of Lagrangian duality to design neural networks that can
accommodate fairness constraints while guaranteeing the privacy of sensitive
attributes. The paper analyses the tension between accuracy, privacy, and
fairness and the experimental evaluation illustrates the benefits of the
proposed model on several prediction tasks.
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