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.- Anthology ID:
- 2024.hcinlp-1.8
- Volume:
- Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Su Lin Blodgett, Amanda Cercas Curry, Sunipa Dey, Michael Madaio, Ani Nenkova, Diyi Yang, Ziang Xiao
- Venues:
- HCINLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 82–88
- Language:
- URL:
- https://aclanthology.org/2024.hcinlp-1.8
- DOI:
- Cite (ACL):
- Sanjana Gautam and Mukund Srinath. 2024. Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP. In Proceedings of the Third Workshop on Bridging Human--Computer Interaction and Natural Language Processing, pages 82–88, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLP (Gautam & Srinath, HCINLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.hcinlp-1.8.pdf