# Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations

arXiv: https://arxiv.org/abs/2110.10980 · HTML: https://mlsanity.com/paper/2110.10980/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2021.

## Built on by (most cited first, 14 of 14)

- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [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)
- [System Cards for AI-Based Decision-Making for Public Policy](https://mlsanity.com/paper/2203.04754/) (2022)
- [An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring](https://mlsanity.com/paper/2201.09151/) (2022)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [It is not "accuracy vs. explainability" -- we need both for trustworthy AI systems](https://mlsanity.com/paper/2212.11136/) (2022)
- [Fairness in Ranking under Disparate Uncertainty](https://mlsanity.com/paper/2309.01610/) (2023)
- [Towards a Reference Software Architecture for Human-AI Teaming in Smart Manufacturing](https://mlsanity.com/paper/2201.04876/) (2022)
- [ReportLogic: Evaluating Logical Quality in Deep Research Reports](https://mlsanity.com/paper/2602.18446/) (2026)
- [Pramana: A Protocol-Layer Treatment of Claim Verification in Autonomous Agent Networks](https://mlsanity.com/paper/2605.20312/) (2026)
- [The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems](https://mlsanity.com/paper/2407.05341/) (2024)
- [Enhancing Trust Through Standards: A Comparative Risk-Impact Framework for Aligning ISO AI Standards with Global Ethical and Regulatory Contexts](https://mlsanity.com/paper/2504.16139/) (2025)
- [Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents](https://mlsanity.com/paper/2605.16281/) (2026)

## Abstract (arXiv)

Important decisions that impact human lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems (ADMS) can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing (EBA) as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity's present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS.