Outputs (2)

Investigating Markers and Drivers of Gender Bias in Machine Translations (2023)
Presentation / Conference Contribution
Barclay, P., & Sami, A. (2024, March). Investigating Markers and Drivers of Gender Bias in Machine Translations. Presented at IEEE International Conference on Software Analysis, Evolution and Reengineering, Rovaniemi, Finland

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such b... Read More about Investigating Markers and Drivers of Gender Bias in Machine Translations.

A case study of fairness in generated images of Large Language Models for Software Engineering tasks (2023)
Presentation / Conference Contribution
Sami, M., Sami, A., & Barclay, P. (2023). A case study of fairness in generated images of Large Language Models for Software Engineering tasks. In 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME). https://doi.org/10.1109/i

Bias in Large Language Models (LLMs) has significant implications. Since they have revolutionized content creation on the web, they can lead to more unfair outcomes, lack of inclusivity, reinforcement of stereotypes and ethical and legal concerns. No... Read More about A case study of fairness in generated images of Large Language Models for Software Engineering tasks.