Papers by Mirko Lai
Are you sure? Measuring models bias in content moderation through uncertainty (2025.findings-emnlp)
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| Challenge: | Language Model-based classifiers perpetuate racial and social biases in content moderation . et al., j. n. d., and j neil, e. c. (2005) measure the fairness of content moderated models . |
| Approach: | They propose an unsupervised approach that benchmarks models on their uncertainty . they use uncertainty as a proxy to analyze the bias of 11 models against women and non-whites . |
| Outcome: | The proposed method analyzes the bias of 11 models against women and non-white annotators . it shows that some pre-trained models predict with high accuracy the labels coming from minority groups . |
Application and Analysis of a Multi-layered Scheme for Irony on the Italian Twitter Corpus TWITTIRÒ (L18-1)
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| Challenge: | Using a multi-layered scheme for the fine-grained annotation of irony on Italian Twitter is a challenging task to be performed by both human annotators and automatic NLP systems. |
| Approach: | They propose to apply a multi-layered scheme for the fine-grained annotation of irony to an Italian Twitter corpus. |
| Outcome: | The proposed scheme can be validated on Italian irony-laden social media contents and is available in the cross- and multi-lingual perspective. |