“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset
Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, Adina Williams
Abstract
As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presuppose which types of biases models can exhibit. In this work, we present a new, more inclusive bias measurement dataset, HolisticBias, which includes nearly 600 descriptor terms across 13 different demographic axes. HolisticBias was assembled in a participatory process including experts and community members with lived experience of these terms. These descriptors combine with a set of bias measurement templates to produce over 450,000 unique sentence prompts, which we use to explore, identify, and reduce novel forms of bias in several generative models. We demonstrate that HolisticBias is effective at measuring previously undetectable biases in token likelihoods from language models, as well as in an offensiveness classifier. We will invite additions and amendments to the dataset, which we hope will serve as a basis for more easy-to-use and standardized methods for evaluating bias in NLP models.- Anthology ID:
- 2022.emnlp-main.625
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9180–9211
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.625
- DOI:
- 10.18653/v1/2022.emnlp-main.625
- Cite (ACL):
- Eric Michael Smith, Melissa Hall, Melanie Kambadur, Eleonora Presani, and Adina Williams. 2022. “I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9180–9211, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- “I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset (Smith et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.625.pdf