The double edge of generative AI: identifying and addressing gender bias through LLMs
Ferreri, Valentina (A.A. 2024/2025) The double edge of generative AI: identifying and addressing gender bias through LLMs. Tesi di Laurea in Business and marketing analytics, Luiss Guido Carli, relatore Andrea De Mauro, pp. 38. [Master's Degree Thesis]
|
PDF (Full text)
Download (869kB) | Preview |
Abstract/Index
Literature review. Defining bias. Gender bias. Gender bias in AI research. Large language models and their role in reinforcing bias. Implications of ai system bias. Mitigating bias in AI. Research gap. Methodology. System design and flow architecture. Prompt engineering. Evaluation procedure. Datasets and their roles. Prototype development: from model to user centered application. Results and discussion.
References
Bibliografia: pp. 29-32.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Marketing (LM-77) |
| Chair: | Business and marketing analytics |
| Thesis Supervisor: | De Mauro, Andrea |
| Thesis Co-Supervisor: | Monsurrò, Luigi |
| Academic Year: | 2024/2025 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 07 Aug 2025 12:08 |
| Last Modified: | 07 Aug 2025 12:08 |
| URI: | https://tesi.luiss.it/id/eprint/43098 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
