FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment
Abstract
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. The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Aditionally, the demo includes a new algorithm (SensitiveNets) for discrimination-aware learning which eliminates sensitive information in our multimodal AI framework.
Keywords
Cite
@article{arxiv.2009.07025,
title = {FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment},
author = {Alejandro Peña and Ignacio Serna and Aythami Morales and Julian Fierrez},
journal= {arXiv preprint arXiv:2009.07025},
year = {2020}
}
Comments
ACM Intl. Conf. on Multimodal Interaction (ICMI). arXiv admin note: substantial text overlap with arXiv:2004.07173