English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep neural networks often learn unintended bias during training, which mighthave harmful effects when deployed in real-world settings. This work surveys214 papers related to sociodemographic bias in natural language processing(NLP). In this study, we aim to provide a more comprehensive understanding ofthe similarities and differences among approaches to sociodemographic bias inNLP. To better understand the distinction between bias and real-world harm, weturn to ideas from psychology and behavioral economics to propose a definitionfor sociodemographic bias. We identify three main categories of NLP biasresearch: types of bias, quantifying bias, and debiasing techniques. Wehighlight the current trends in quantifying bias and debiasing techniques,offering insights into their strengths and weaknesses. We conclude that currentapproaches on quantifying bias face reliability issues, that many of the biasmetrics do not relate to real-world bias, and that debiasing techniques need tofocus more on training methods. Finally, we provide recommendations for futurework.

Survey on Sociodemographic Bias in Natural Language Processing