Human-centric multimodal machine learning: Recent advances and testbed on AI-based recruitment
The presence of decision-making algorithms in society is rapidly increasing nowadays,
while concerns about their transparency and the possibility of these algorithms becoming …
while concerns about their transparency and the possibility of these algorithms becoming …
Demographic bias in biometrics: A survey on an emerging challenge
P Drozdowski, C Rathgeb, A Dantcheva… - … on Technology and …, 2020 - ieeexplore.ieee.org
Systems incorporating biometric technologies have become ubiquitous in personal,
commercial, and governmental identity management applications. Both cooperative (eg …
commercial, and governmental identity management applications. Both cooperative (eg …
A comprehensive study on face recognition biases beyond demographics
Face recognition (FR) systems have a growing effect on critical decision-making processes.
Recent works have shown that FR solutions show strong performance differences based on …
Recent works have shown that FR solutions show strong performance differences based on …
Face recognition: too bias, or not too bias?
We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR)
systems using a novel Balanced Faces in the Wild (BFW) dataset: data balanced for gender …
systems using a novel Balanced Faces in the Wild (BFW) dataset: data balanced for gender …
SensitiveNets: Learning agnostic representations with application to face images
This work proposes a novel privacy-preserving neural network feature representation to
suppress the sensitive information of a learned space while maintaining the utility of the …
suppress the sensitive information of a learned space while maintaining the utility of the …
InsideBias: Measuring bias in deep networks and application to face gender biometrics
This work explores the biases in learning processes based on deep neural network
architectures. We analyze how bias affects deep learning processes through a toy example …
architectures. We analyze how bias affects deep learning processes through a toy example …
[HTML][HTML] Sensitive loss: Improving accuracy and fairness of face representations with discrimination-aware deep learning
We propose a discrimination-aware learning method to improve both the accuracy and
fairness of biased face recognition algorithms. The most popular face recognition …
fairness of biased face recognition algorithms. The most popular face recognition …
Bias in multimodal AI: Testbed for fair automatic recruitment
The presence of decision-making algorithms in society is rapidly increasing nowadays,
while concerns about their transparency and the possibility of these algorithms becoming …
while concerns about their transparency and the possibility of these algorithms becoming …
Leave-one-out unfairness
E Black, M Fredrikson - Proceedings of the 2021 ACM Conference on …, 2021 - dl.acm.org
We introduce leave-one-out unfairness, which characterizes how likely a model's prediction
for an individual will change due to the inclusion or removal of a single other person in the …
for an individual will change due to the inclusion or removal of a single other person in the …
Algorithmic discrimination: Formulation and exploration in deep learning-based face biometrics
The most popular face recognition benchmarks assume a distribution of subjects without
much attention to their demographic attributes. In this work, we perform a comprehensive …
much attention to their demographic attributes. In this work, we perform a comprehensive …