Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

• URL: http://arxiv.org/abs/2209.10335v2
• Date: Thu, 22 Sep 2022 13:08:04 GMT
• Title: Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling
• Authors: Thiemo Wambsganss, Vinitra Swamy, Roman Rietsche, Tanja K\"aser
• Abstract summary: We analyze bias across text and through multiple architectures on a corpus of 9,165 German peer- reviews over five years. Our collected corpus does not reveal many biases in the co-occurrence analysis or in the GloVe embeddings. Pre-trained German language models find substantial conceptual, racial, and gender bias.
• Score: 10.080007569933331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in different domains, they are limited in addressing fine-grained analysis on educational and multilingual corpora. In this work, we analyze bias across text and through multiple architectures on a corpus of 9,165 German peer-reviews collected from university students over five years. Notably, our corpus includes labels such as helpfulness, quality, and critical aspect ratings from the peer-review recipient as well as demographic attributes. We conduct a Word Embedding Association Test (WEAT) analysis on (1) our collected corpus in connection with the clustered labels, (2) the most common pre-trained German language models (T5, BERT, and GPT-2) and GloVe embeddings, and (3) the language models after fine-tuning on our collected data-set. In contrast to our initial expectations, we found that our collected corpus does not reveal many biases in the co-occurrence analysis or in the GloVe embeddings. However, the pre-trained German language models find substantial conceptual, racial, and gender bias and have significant changes in bias across conceptual and racial axes during fine-tuning on the peer-review data. With our research, we aim to contribute to the fourth UN sustainability goal (quality education) with a novel dataset, an understanding of biases in natural language education data, and the potential harms of not counteracting biases in language models for educational tasks.

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