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

arXiv: https://arxiv.org/abs/2506.22774 · HTML: https://mlsanity.com/paper/2506.22774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Holistic Evaluation of Language Models](https://mlsanity.com/paper/2211.09110/) (2022)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance](https://mlsanity.com/paper/2206.11922/) (2022)
- [Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI](https://mlsanity.com/paper/2106.01410/) (2021)
- [Ethics of AI: A Systematic Literature Review of Principles and Challenges](https://mlsanity.com/paper/2109.07906/) (2021)
- [Linking Artificial Intelligence Principles](https://mlsanity.com/paper/1812.04814/) (2018)
- [secml: A Python Library for Secure and Explainable Machine Learning](https://mlsanity.com/paper/1912.10013/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Critical Analysis of Trustworthy AI Tools, Mark Frameworks, and the Implementation Chasms](https://mlsanity.com/paper/2607.15480/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) technology epitomizes the complex challenges posed by human-made artifacts, particularly those widely integrated into society and exerting 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.