Sociodemographic Bias in Language Models: A Survey and Forward Path
CoRR(2023)
Abstract
This paper presents a comprehensive survey of work on sociodemographic bias
in language models (LMs). Sociodemographic biases embedded within language
models can have harmful effects when deployed in real-world settings. We
systematically organize the existing literature into three main areas: types of
bias, quantifying bias, and debiasing techniques. We also track the evolution
of investigations of LM bias over the past decade. We identify current trends,
limitations, and potential future directions in bias research. To guide future
research towards more effective and reliable solutions, we present a checklist
of open questions. We also recommend using interdisciplinary approaches to
combine works on LM bias with an understanding of the potential harms.
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