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. In this
paper, we propose a unifying conceptualization: the predictive bias framework
for NLP. We summarize the NLP literature and propose a general mathematical
definition of predictive bias in NLP along with a conceptual framework,
differentiating four main origins of biases: label bias, selection bias, model
overamplification, and semantic bias. We discuss how past work has countered
each bias origin. Our framework serves to guide an introductory overview of
predictive bias in NLP, integrating existing work into a single structure and
opening avenues for future research.

文章提出了一种用于 NLP（自然语言处理）的预测性偏差框架，该框架将数据、模型、标签偏差、选择偏差、模型过度放大、语义偏差作为偏差的四个主要来源，并探讨了过去的工作是如何对抗每种偏差来源的，以期引导未来研究。