Ethical Risk Assessment of the Data Harnessing Process of LLM supported on Consensus of Well-known Multi-Ethical Frameworks

Abstract

The rapid advancements in large language models (LLMs) have revolutionized natural language processing, unlocking unprecedented capabilities in communication, automation, and knowledge generation. However, the ethical implications of LLM development, particularly in data harnessing, remain a critical challenge. Despite widespread discussion about the ethical compliance of LLMs -- especially concerning their data harnessing processes, there remains a notable absence of concrete frameworks to systematically guide or measure the ethical risks involved. In this paper we discuss a potential pathway for building an Ethical Risk Scoring (ERS) system to quantitatively assess the ethical integrity of the data harnessing process for AI systems. This system is based on a set of assessment questions grounded in core ethical principles, which are, in turn, supported by commanding ethical theories. By integrating measurable scoring mechanisms, this approach aims to foster responsible LLM development, balancing technological innovation with ethical accountability.