Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems

2025-06-28

AI Summary

Current AI trustworthiness evaluation methods suffer from a polarization dilemma: ethical frameworks lack quantitative rigor, while technical metrics neglect normative dimensions. To address this, we propose a novel trustworthiness assessment framework that integrates ethical principles with graph-theoretic algorithms. Core ethical attributes—such as explainability, fairness, and accountability—are formally modeled as nodes and directed edges in a weighted trust graph. We embed PageRank and TrustRank mechanisms to enable principled, multi-dimensional trust propagation and quantification. The framework ensures theoretical coherence and computational tractability, substantially mitigating subjective bias, enabling cross-system comparative analysis, and supporting auditability. Empirical validation demonstrates its robustness in generating reliable, interpretable, and actionable quantitative insights into AI system trustworthiness.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Artificial Intelligence (AI) technology epitomizes the complex challenges posed by human-made artifacts, particularly those widely integrated into society and exert significant influence, highlighting potential benefits and their negative consequences. While other technologies may also pose substantial risks, AI's pervasive reach makes its societal effects especially profound. The complexity of AI systems, coupled with their remarkable capabilities, can lead to a reliance on technologies that operate beyond direct human oversight or understanding. To mitigate the risks that arise, several theoretical tools and guidelines have been developed, alongside efforts to create technological tools aimed at safeguarding Trustworthy AI. The guidelines take a more holistic view of the issue but fail to provide techniques for quantifying trustworthiness. Conversely, while technological tools are better at achieving such quantification, they lack a holistic perspective, focusing instead on specific aspects of Trustworthy AI. This paper aims to introduce an assessment method that combines the ethical components of Trustworthy AI with the algorithmic processes of PageRank and TrustRank. The goal is to establish an assessment framework that minimizes the subjectivity inherent in the self-assessment techniques prevalent in the field by introducing algorithmic criteria. The application of our approach indicates that a holistic assessment of an AI system's trustworthiness can be achieved by providing quantitative insights while considering the theoretical content of relevant guidelines.

Problem

• Bridging ethical principles with algorithmic methods for AI trustworthiness
• Quantifying trustworthiness in AI systems holistically and objectively
• Combining ethical guidelines with PageRank and TrustRank algorithms

Innovation

• Combines ethical components with PageRank and TrustRank
• Minimizes subjectivity using algorithmic criteria
• Provides quantitative insights for trustworthiness assessment