@inproceedings{gautam-srinath-2024-blind,
title = "Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in {NLP}",
author = "Gautam, Sanjana and
Srinath, Mukund",
editor = "Blodgett, Su Lin and
Curry, Amanda Cercas and
Dev, Sunipa and
Madaio, Michael and
Nenkova, Ani and
Yang, Diyi and
Xiao, Ziang",
booktitle = "Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.hcinlp-1.8",
doi = "10.18653/v1/2024.hcinlp-1.8",
pages = "82--88",
abstract = "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.",
}
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%0 Conference Proceedings
%T Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP
%A Gautam, Sanjana
%A Srinath, Mukund
%Y Blodgett, Su Lin
%Y Curry, Amanda Cercas
%Y Dev, Sunipa
%Y Madaio, Michael
%Y Nenkova, Ani
%Y Yang, Diyi
%Y Xiao, Ziang
%S Proceedings of the Third Workshop on Bridging Human–Computer Interaction and Natural Language Processing
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F gautam-srinath-2024-blind
%X 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.
%R 10.18653/v1/2024.hcinlp-1.8
%U https://aclanthology.org/2024.hcinlp-1.8
%U https://doi.org/10.18653/v1/2024.hcinlp-1.8
%P 82-88
Markdown (Informal)
[Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP](https://aclanthology.org/2024.hcinlp-1.8) (Gautam & Srinath, HCINLP-WS 2024)
ACL