# Ethical AI Governance: Methods for Evaluating Trustworthy AI

arXiv: https://arxiv.org/abs/2409.07473 · HTML: https://mlsanity.com/paper/2409.07473/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [The Foundation Model Transparency Index](https://mlsanity.com/paper/2310.12941/) (2023)
- [Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling](https://mlsanity.com/paper/2402.17861/) (2024)
- [The Role of Explainability in Assuring Safety of Machine Learning in Healthcare](https://mlsanity.com/paper/2109.00520/) (2021)
- [Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering](https://mlsanity.com/paper/2209.04963/) (2022)
- [Improving Fairness and Privacy in Selection Problems](https://mlsanity.com/paper/2012.03812/) (2020)
- [Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice](https://mlsanity.com/paper/2306.06542/) (2023)
- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [A Transparency Index Framework for AI in Education](https://mlsanity.com/paper/2206.03220/) (2022)
- [Towards a Framework for Certification of Reliable Autonomous Systems](https://mlsanity.com/paper/2001.09124/) (2020)
- [Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness](https://mlsanity.com/paper/2206.00474/) (2022)
- [Toward a Taxonomy of Trust for Probabilistic Machine Learning](https://mlsanity.com/paper/2112.03270/) (2021)
- [From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Models](https://mlsanity.com/paper/2402.15770/) (2024)
- [An End-To-End Machine Learning Pipeline That Ensures Fairness Policies](https://mlsanity.com/paper/1710.06876/) (2017)
- [Developing a novel fair-loan-predictor through a multi-sensitive debiasing pipeline: DualFair](https://mlsanity.com/paper/2110.08944/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Exploring the Ethical Concerns in User Reviews of Mental Health Apps using Topic Modeling and Sentiment Analysis](https://mlsanity.com/paper/2602.18454/) (2026)

## Abstract (arXiv)

Trustworthy Artificial Intelligence (TAI) integrates ethics that align with human values, looking at their influence on AI behaviour and decision-making. Primarily dependent on self-assessment, TAI evaluation aims to ensure ethical standards and safety in AI development and usage. This paper reviews the current TAI evaluation methods in the literature and offers a classification, contributing to understanding self-assessment methods in this field.