AI Ethical Framework: A Government-Centric Tool Using Generative AI

Artificial Intelligence (AI) is transforming industries and societies globally. To fully harness this advancement, it is crucial for countries to integrate AI across different domains. Moral relativism in AI ethics suggests that as ethical norms vary significantly across societies, frameworks guiding AI development should be context-specific, reflecting the values, norms, and beliefs of the cultures where these technologies are deployed. To address this challenge, we introduce an intuitive, generative AI based solution that could help governments establish local ethical principles for AI software and ensure adherence to these standards. We propose two web applications: one for government use and another for software developers. The government-centric application dynamically calibrates ethical weights across domains such as the economy, education, and healthcare according to sociocultural context. By using LLMs, this application enables the creation of a tailored ethical blueprint for each domain or context, helping each country or region better define its core values. For developers, we propose a diagnostic application that actively checks software, assessing its alignment with the ethical principles established by the government. This feedback allows developers to recalibrate their AI applications, ensuring they are both efficient and ethically suitable for the intended area of use. In summary, this paper presents a tool utilizing LLMs to adapt software development to the ethical and cultural principles of a specific society.

@article{Kone2024,
  title     = {AI Ethical Framework: A Government-Centric Tool Using Generative AI},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Lalla Aicha Kone and Anna Ouskova Leonteva and Mamadou Tourad Diallo and Ahmedou Haouba and Pierre COLLET},
  doi       = {10.14569/IJACSA.2024.0151108},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151108}
}