Responsible AI Framework for Generative AI: “Data That Can Be Trusted” - Eranpurwala, Femida (Digital Repository of Theses - SSBM Geneva, 2025)
Background The rapid expansion of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has brought transformative value across numerous industries. Generative AI is one of the most important technologies in AI. It uses LLMs to produce content based on the user’s request, often working at par or better than humans. Even after improvement and various achievements, LLMs still remain quite opaque. This raises questions related to transparency, credibility and reliability. Because of these risks, a Responsible AI Framework is required. These risks highlight the need to have a framework that ensures AI technologies follow ethical and legal principles, especially fairness and privacy. Existing frameworks provide guidance but are mostly qualitative, lacking quantitative approaches for real world use. This research aims to contribute toward an operational framework to assess LLM output using Responsible AI principles. A “LLMRESAI - Responsible AI Score” is introduced to evaluate LLMs on fairness and privacy. In future, this score will help build trust and support ethical AI development. Methods This is a quantitative study of the “LLMRESAI – Responsible AI Score” to measure fairness and privacy. GPT-Neo was chosen to generate outputs using datasets covering various demographics, privacy-sensitive cases, and fact-checked content. To test if LLMRESAI is effective, classical metrics like PII and WEAT are used. These give binary results, but the proposed framework goes beyond that. It creates a normalized score using multiple inputs, showing how fair or private the output is. Flagged issues like privacy violations or biased outputs are matched with the LLMRESAI Score. This helps give a more continuous and detailed measurement instead of a simple true/false check. Results The LLMRESAI Score outperformed existing metrics for fairness and privacy. It was more consistent, aligned better with human judgment, and was more scalable with GPT-Neo outputs. Discussion and Conclusion LLMRESAI is a practical and useful metric. It fills the gap between high level Responsible AI ideas and real implementation. The results show strong potential to support ethical and reliable AI. It also opens doors for future research on scalable, quantitative AI frameworks.