Attesting Biases and Discrimination using Language Semantics
September 10, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Xavier Ferrer Aran, Jose M. Such, Natalia Criado
arXiv ID
1909.04386
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CL
Citations
7
Venue
arXiv.org
Last Checked
4 months ago
Abstract
AI agents are increasingly deployed and used to make automated decisions that affect our lives on a daily basis. It is imperative to ensure that these systems embed ethical principles and respect human values. We focus on how we can attest to whether AI agents treat users fairly without discriminating against particular individuals or groups through biases in language. In particular, we discuss human unconscious biases, how they are embedded in language, and how AI systems inherit those biases by learning from and processing human language. Then, we outline a roadmap for future research to better understand and attest problematic AI biases derived from language.
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