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

With the rise of online hate speech, automatic detection of Hate Speech,Offensive texts as a natural language processing task is getting popular.However, very little research has been done to detect unintended social biasfrom these toxic language datasets. This paper introduces a new datasetToxicBias curated from the existing dataset of Kaggle competition named "JigsawUnintended Bias in Toxicity Classification". We aim to detect social biases,their categories, and targeted groups. The dataset contains instances annotatedfor five different bias categories, viz., gender, race/ethnicity, religion,political, and LGBTQ. We train transformer-based models using our curateddatasets and report baseline performance for bias identification, targetgeneration, and bias implications. Model biases and their mitigation are alsodiscussed in detail. Our study motivates a systematic extraction of social biasdata from toxic language datasets. All the codes and dataset used forexperiments in this work are publicly available

Detecting Unintended Social Bias in Toxic Language Datasets