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What is needed to overcome and regulate bias in NLP technology? For NLP technology providers, there is a need to improve the diversity in underlying training data, as well as increase research into methods to improve performance for low resource scenarios, such as under-represented languages, dialects, ages or genders.
Apr 27, 2022
May 10, 2021 · A series that explores ways to mitigate possible biases and create a pathway toward greater fairness in AI and emerging technologies.
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One of the most central ethical issues in NLP is the impact of hidden biases that affect performance unevenly, and thereby disadvantage certain user groups.
Reduce Model Bias These algorithms produce encouraging results and are unquestionably a positive step in the fight against NLP bias. Even though the discipline ...
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Feb 27, 2021 · Many papers suggest different strategies for addressing bias in NLP. The bias can be reduced by resampling training data from a larger dataset ...
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Jan 8, 2024 · Mitigating bias in NLP involves using diverse and representative training data, implementing bias detection tools, applying debiasing ...
Nov 29, 2021 · Here is an easy guide to understand how bias in natural language processing works. We explain why sexist technologies like search engines are not just an ...
Mar 29, 2021 · To quantify bias within word embeddings, NLP researchers have developed a number of bias identification methods. An early “bias direction” ...
Jun 1, 2023 · Bias detection in NLP (Natural Language Processing) refers to the identification and mitigation of biases present in text data or NLP models.
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Aug 20, 2021 · We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and ...