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Found 2 papers, 2 papers with code
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments
Building on current work on multilingual hate speech (e. g., Ousidhoum et al. (2019)) and hate speech reduction (e. g., Sap et al. (2020)), we present XTREMESPEECH, a new hate speech dataset containing 20, 297 social media passages from Brazil, Germany, India and Kenya.
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
In this paper, we first demonstrate a surprising finding: pretrained language models recognize, to a considerable degree, their undesirable biases and the toxicity of the content they produce.
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