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

Understand

An increasing number of works in natural language processing have addressed the effect of bias on the predicted outcomes, introducing mitigation techniques that act on different parts of the standard NLP pipeline (data and models).

• However, these works have been conducted in isolation, without a unifying framework to organize efforts within the field.
• This leads to repetitive approaches, and puts an undue focus on the effects of bias, rather than on their origins.
• Research focused on bias symptoms rather than the underlying origins could limit the development of effective countermeasures.