A case study of fairness in generated images of Large Language Models for Software Engineering tasks

Sami, Mansour; Sami, Ashkan; Barclay, Pete

Abstract

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. Notably, OpenAI has recently made claims they have introduced a new technique to ensure that DALL-E-2 generates images of people accurately reflect the diversity of the world’s population. In order to investigate bias within the field of Software Engineering, the study utilized DALL-E-2 image generation to assess 56 tasks related to software engineering. Another objective was to determine the impact of OpenAI’s new measures on the generated images for these specific tasks. Two sets of experiments were conducted. In one set, the tasks were prefixed with the clause "As a Software Engineer," while in the other set, only the tasks themselves were used. The tasks were presented in a gender-neutral manner, and the AI was instructed to generate images for each task 20 times. For a female-dominant task of doing administrative tasks, 40 more images were generated. The study revealed a large gender bias in the 2,280 images generated. For instance, in the subset of experiments with prompts explicitly incorporating the phrase "As a software engineer," only 2% of the generated images portrayed female protagonists. In all the images in this setting, male protagonists were dominant and in 45 tasks 100% of the protagonists were male. Notably, images generated without the prefixed clause only had more female protagonists in ‘provide comments on project milestones’ and ‘provide enhancements’, while other tasks did not exhibit a similar pattern. The findings emphasize unsuitability of implemented guardrails and the importance of further research on LLMs assessments. Further research is needed in LLMs to find out where their guardrails fail so companies can address them properly.

Citation

Sami, M., Sami, A., & Barclay, P. (2023, October). A case study of fairness in generated images of Large Language Models for Software Engineering tasks. Presented at 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME), Bogotá, Colombia

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)
• Start Date: Oct 1, 2023
• End Date: Oct 6, 2023
• Online Publication Date: Dec 11, 2023
• Publication Date: 2023
• Deposit Date: Apr 18, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 2576-3148
• Book Title: 2023 IEEE International Conference on Software Maintenance and Evolution (ICSME)
• DOI: https://doi.org/10.1109/icsme58846.2023.00051
• Keywords: Large Language Models, bias, gender diversity, Generative images, DALL-E-2
• Public URL: http://researchrepository.napier.ac.uk/Output/3524858