  Although much work in NLP has focused on measuring and mitigating
stereotypical bias in semantic spaces, research addressing bias in
computational argumentation is still in its infancy. In this paper, we address
this research gap and conduct a thorough investigation of bias in argumentative
language models. To this end, we introduce ABBA, a novel resource for bias
measurement specifically tailored to argumentation. We employ our resource to
assess the effect of argumentative fine-tuning and debiasing on the intrinsic
bias found in transformer-based language models using a lightweight
adapter-based approach that is more sustainable and parameter-efficient than
full fine-tuning. Finally, we analyze the potential impact of language model
debiasing on the performance in argument quality prediction, a downstream task
of computational argumentation. Our results show that we are able to
successfully and sustainably remove bias in general and argumentative language
models while preserving (and sometimes improving) model performance in
downstream tasks. We make all experimental code and data available at
https://github.com/umanlp/FairArgumentativeLM.
