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Found 5 papers, 2 papers with code
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide
Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion.
Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset
Understanding and quantifying the bias introduced by human annotation of data is a crucial problem for trustworthy supervised learning.
Fine-Grained Fairness Analysis of Abusive Language Detection Systems with CheckList
Our evaluation shows that, although BERT-based classifiers achieve high accuracy levels on a variety of natural language processing tasks, they perform very poorly as regards fairness and bias, in particular on samples involving implicit stereotypes, expressions of hate towards minorities and protected attributes such as race or sexual orientation.
FairBelief - Assessing Harmful Beliefs in Language Models
Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing.
Social Bias Probing: Fairness Benchmarking for Language Models
While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models.
