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

2025-05-19

Canadian AI

AI Summary

This work addresses the current lack of a systematic framework for evaluating ethical risks in data collection practices for large language models (LLMs). It proposes the first quantifiable assessment framework that integrates multiple prominent ethical theories, structuring evaluation around core ethical principles through a set of targeted questions and establishing a scoring system to measure ethical risk. This approach enables systematic, quantitative ethical auditing of LLM data curation processes. By offering a practical tool for assessing ethical compliance in AI development, the framework fills a critical gap in existing research—particularly in the integration of diverse ethical theories and the empirical evaluation of real-world data practices—thereby advancing the responsible development of artificial intelligence.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• ethical risk
• data harnessing
• large language models
• ethical assessment
• AI ethics

Innovation

• Ethical Risk Scoring
• Large Language Models
• Data Harnessing
• Ethical Frameworks
• Quantitative Assessment