Can querying for bias leak protected attributes? achieving privacy with smooth sensitivity
Existing regulations often prohibit model developers from accessing protected attributes
(gender, race, etc.) during training. This leads to scenarios where fairness assessments …
(gender, race, etc.) during training. This leads to scenarios where fairness assessments …
Fairness without demographic data: A survey of approaches
C Ashurst, A Weller - Proceedings of the 3rd ACM Conference on Equity …, 2023 - dl.acm.org
Detecting, measuring and mitigating various measures of unfairness are core aims of
algorithmic fairness research. However, the most prominent approaches require access to …
algorithmic fairness research. However, the most prominent approaches require access to …
How to select physics-informed neural networks in the absence of ground truth: a Pareto front-based strategy
Physics-informed neural networks (PINNs) have been proposed as a potential route to
inverse modelling or mesh-free alternative to numerical methods for partial differential …
inverse modelling or mesh-free alternative to numerical methods for partial differential …
Fair Classifiers Without Fair Training: An Influence-Guided Data Sampling Approach
A fair classifier should ensure the benefit of people from different groups, while the group
information is often sensitive and unsuitable for model training. Therefore, learning a fair …
information is often sensitive and unsuitable for model training. Therefore, learning a fair …
[PDF][PDF] A Survey on Fairness Without Demographics
The issue of bias in Machine Learning (ML) models is a significant challenge for the
machine learning community. Real-world biases can be embedded in the data used to train …
machine learning community. Real-world biases can be embedded in the data used to train …