# Ethics-Based Auditing to Develop Trustworthy AI

arXiv: https://arxiv.org/abs/2105.00002 · HTML: https://mlsanity.com/paper/2105.00002/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)

## Built on by (most cited first, 23 of 23)

- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation](https://mlsanity.com/paper/2111.05071/) (2021)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie](https://mlsanity.com/paper/2309.09120/) (2023)
- [Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society](https://mlsanity.com/paper/2403.07904/) (2024)
- [High-Risk AI Systems and the Problem of Identity in the European AI Act](https://mlsanity.com/paper/2605.23922/) (2026)
- [FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines](https://mlsanity.com/paper/2307.15475/) (2023)
- [Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study](https://mlsanity.com/paper/2411.08906/) (2024)
- [The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance](https://mlsanity.com/paper/2512.11933/) (2025)
- [Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making](https://mlsanity.com/paper/2405.16424/) (2024)
- [A Comparative Analysis of Ethical and Safety Gaps in LLMs using Relative Danger Coefficient](https://mlsanity.com/paper/2505.04654/) (2025)
- [Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial Intelligence](https://mlsanity.com/paper/2309.14617/) (2023)
- [A Capability Approach to AI Ethics](https://mlsanity.com/paper/2502.03469/) (2025)
- [Trustworthy AI Posture (TAIP): A Framework for Continuous AI Assurance of Agentic Systems at Horizontal and Vertical scale](https://mlsanity.com/paper/2603.03340/) (2026)
- [A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance](https://mlsanity.com/paper/2607.16130/) (2026)
- [Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education](https://mlsanity.com/paper/2109.07908/) (2021)
- [Measuring a Texts Fairness Dimensions Using Machine Learning Based on Social Psychological Factors](https://mlsanity.com/paper/2111.00086/) (2021)
- [Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears](https://mlsanity.com/paper/2504.05008/) (2025)
- [Framework, Standards, Applications and Best practices of Responsible AI : A Comprehensive Survey](https://mlsanity.com/paper/2504.13979/) (2025)
- [Towards explainable decision support using hybrid neural models for logistic terminal automation](https://mlsanity.com/paper/2509.07577/) (2025)
- [The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits](https://mlsanity.com/paper/2607.02201/) (2026)

## Abstract (arXiv)

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation.