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

TL;DR

This paper proposes an Ethical Risk Scoring system for LLM data harnessing, grounded in well-known ethical frameworks, to quantitatively evaluate and promote responsible AI development.

Contribution

It introduces a systematic, measurable approach to assess ethical risks in LLM data processes using consensus from established multi-ethical frameworks.

Findings

Proposes a novel ERS system based on ethical principles.

Integrates measurable scoring mechanisms for ethical assessment.

Aims to guide responsible LLM development.

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.