Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview

Deven Santosh Shah, H. Andrew Schwartz, Dirk Hovy


Abstract
An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.
Anthology ID:
2020.acl-main.468
Original:
2020.acl-main.468v1
Version 2:
2020.acl-main.468v2
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5248–5264
Language:
URL:
https://aclanthology.org/2020.acl-main.468
DOI:
10.18653/v1/2020.acl-main.468
Bibkey:
Cite (ACL):
Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020. Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5248–5264, Online. Association for Computational Linguistics.
Cite (Informal):
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (Shah et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.468.pdf
Video:
 http://slideslive.com/38929459