Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP

Fuente: arXiv
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Autori principali: Gautam, Sanjana, Srinath, Mukund
Natura: Preprint
Pubblicazione: 2024
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author Gautam, Sanjana
Srinath, Mukund
author_facet Gautam, Sanjana
Srinath, Mukund
contents With the rapid proliferation of artificial intelligence, there is growing concern over its potential to exacerbate existing biases and societal disparities and introduce novel ones. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. While evidence suggests that integrating human perspectives can mitigate bias-related issues in AI systems, it also introduces challenges associated with cognitive biases inherent in human decision-making. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.
format Preprint
id arxiv_https___arxiv_org_abs_2404_19071
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP
Gautam, Sanjana
Srinath, Mukund
Human-Computer Interaction
Computation and Language
With the rapid proliferation of artificial intelligence, there is growing concern over its potential to exacerbate existing biases and societal disparities and introduce novel ones. This issue has prompted widespread attention from academia, policymakers, industry, and civil society. While evidence suggests that integrating human perspectives can mitigate bias-related issues in AI systems, it also introduces challenges associated with cognitive biases inherent in human decision-making. Our research focuses on reviewing existing methodologies and ongoing investigations aimed at understanding annotation attributes that contribute to bias.
title Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP
topic Human-Computer Interaction
Computation and Language
url https://arxiv.org/abs/2404.19071