FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment

2020/09/12 by Alejandro Peña, Peña, Alejandro, Ignacio Serna +5 · 2 citations
Computer Science · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Mobile Crowdsensing and Crowdsourcing

paper · pdf · doi:10.48550/arxiv.2009.07025

openalex publication_date 2020/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)
• Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment

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