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Found 8 papers, 6 papers with code
OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning
The reverse-engineered nature of traditional models complicates the enforcement of fairness and can lead to biased outcomes.
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds.
A Fair Pricing Model via Adversarial Learning
In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.
Fairness without the sensitive attribute via Causal Variational Autoencoder
In recent years, most fairness strategies in machine learning models focus on mitigating unwanted biases by assuming that the sensitive information is observed.
Learning Unbiased Representations via Rényi Minimization
We leverage recent work which has been done to estimate this coefficient by learning deep neural network transformations and use it as a minmax game to penalize the intrinsic bias in a multi dimensional latent representation.
Adversarial Learning for Counterfactual Fairness
In recent years, fairness has become an important topic in the machine learning research community.
Fairness-Aware Neural Réyni Minimization for Continuous Features
Second, by minimizing the HGR directly with an adversarial neural network architecture.
