# ''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text

**Authors:** Rishav Hada, Agrima Seth, Harshita Diddee, Kalika Bali  
**arXiv:** [2310.17428](https://arxiv.org/abs/2310.17428)  
**Date:** 2023-10-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.17428v1/

## Key points

- Language, societal belief systems, and gender bias are intricately linked
- Language is a potent tool for expressing societal biases, including gender bias
- Large Language Models (LLMs) can perpetuate biases, highlighting the need for understanding these biases
- The study takes a nuanced approach to investigating gender bias on a relative scale
- Researchers create a dataset of GPT-generated English text with normative ratings of gender bias using Best-Worst Scaling
- Various themes of gender biases are uncovered in the observed ranking, with identity-attack closely linked to gender bias
- Existing automated models trained on related concepts are explored in their performance on the dataset
- Recognizing and addressing gender bias in text-based applications is crucial to mitigate harmful impacts on society
- Leveraging annotator disagreements and frameworks like Best-Worst Scaling contributes valuable insights into combating gender bias in natural language processing
- The study sheds light on the complexities of gender bias in generated English text and emphasizes developing strategies to detect and mitigate such biases effectively

## Summary

In this study, the authors delve into the intricate relationship between language, societal belief systems, and gender bias. They highlight how language serves as a potent tool for the expression of societal biases, including gender bias, which is pervasive in both online and offline discourses. With Large Language Models (LLMs) becoming increasingly adept at generating human-like text, it is crucial to understand the biases these systems can perpetuate. Unlike previous work that often treats gender bias as a binary classification task, this study takes a more nuanced approach by investigating bias on a relative scale. The researchers create a groundbreaking dataset of GPT-generated English text with normative ratings of gender bias using Best-Worst Scaling – an efficient comparative annotation framework. Through systematic analysis, they uncover various themes of gender biases in the observed ranking and identify identity-attack as closely linked to gender bias. Furthermore, the study explores how existing automated models trained on related concepts perform on their dataset. The authors emphasize the importance of recognizing and addressing gender bias in text-based applications to mitigate its harmful impacts on society. By leveraging annotator disagreements and utilizing frameworks like Best-Worst Scaling, this research contributes valuable insights into understanding and combating gender bias in natural language processing. Overall, this study sheds light on the complexities of gender bias in generated English text and underscores the significance of developing strategies to detect and mitigate such biases effectively. The findings offer valuable implications for future research efforts aimed at promoting fairness and inclusivity in AI technologies.