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Found 8 papers, 2 papers with code
Document Layout Annotation: Database and Benchmark in the Domain of Public Affairs
Every day, thousands of digital documents are generated with useful information for companies, public organizations, and citizens.
Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs
The analysis of public affairs documents is crucial for citizens as it promotes transparency, accountability, and informed decision-making.
Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious case study focused on automated recruitment: FairCVtest.
Facial Expressions as a Vulnerability in Face Recognition
This work explores facial expression bias as a security vulnerability of face recognition systems.
Learning Emotional-Blinded Face Representations
We propose two face representations that are blind to facial expressions associated to emotional responses.
FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment
With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest.
Bias in Multimodal AI: Testbed for Fair Automatic Recruitment
With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest.
InsideBias: Measuring Bias in Deep Networks and Application to Face Gender Biometrics
We analyze how bias affects deep learning processes through a toy example using the MNIST database and a case study in gender detection from face images.
