# Argument from Old Man's View: Assessing Social Bias in Argumentation

arXiv: https://arxiv.org/abs/2011.12014 · HTML: https://mlsanity.com/paper/2011.12014/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 9 of 9)

- [Fair and Argumentative Language Modeling for Computational Argumentation](https://mlsanity.com/paper/2204.04026/) (2022)
- [Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling](https://mlsanity.com/paper/2209.10335/) (2022)
- [No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media](https://mlsanity.com/paper/2211.03634/) (2022)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [Differential Bias: On the Perceptibility of Stance Imbalance in Argumentation](https://mlsanity.com/paper/2210.06970/) (2022)
- [Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection](https://mlsanity.com/paper/2502.06487/) (2025)
- [Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness](https://mlsanity.com/paper/2406.09977/) (2024)
- [Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval](https://mlsanity.com/paper/2407.19670/) (2024)
- [Topic Bias in Emotion Classification](https://mlsanity.com/paper/2312.09043/) (2023)

## Abstract (arXiv)

Social bias in language - towards genders, ethnicities, ages, and other social groups - poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. So far, however, little attention has been paid to bias in computational argumentation. In this paper, we study the existence of social biases in large English debate portals. In particular, we train word embedding models on portal-specific corpora and systematically evaluate their bias using WEAT, an existing metric to measure bias in word embeddings. In a word co-occurrence analysis, we then investigate causes of bias. The results suggest that all tested debate corpora contain unbalanced and biased data, mostly in favor of male people with European-American names. Our empirical insights contribute towards an understanding of bias in argumentative data sources.