# Law and the Emerging Political Economy of Algorithmic Audits

arXiv: https://arxiv.org/abs/2406.11855 · HTML: https://mlsanity.com/paper/2406.11855/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 136 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem](https://mlsanity.com/paper/2310.02521/) (2023)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023)
- [Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices](https://mlsanity.com/paper/2006.12358/) (2020)
- [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022)
- [Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits](https://mlsanity.com/paper/2102.04256/) (2021)
- [Auditing Work: Exploring the New York City algorithmic bias audit regime](https://mlsanity.com/paper/2402.08101/) (2024)
- [Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK](https://mlsanity.com/paper/2304.11218/) (2023)
- [Auditing Cross-Cultural Consistency of Human-Annotated Labels for Recommendation Systems](https://mlsanity.com/paper/2305.05917/) (2023)
- [Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries](https://mlsanity.com/paper/2311.12573/) (2023)
- [Bugs in our Pockets: The Risks of Client-Side Scanning](https://mlsanity.com/paper/2110.07450/) (2021)
- [Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest](https://mlsanity.com/paper/2207.08773/) (2022)
- [Auditing Recommender Systems -- Putting the DSA into practice with a risk-scenario-based approach](https://mlsanity.com/paper/2302.04556/) (2023)
- [MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities](https://mlsanity.com/paper/2303.01998/) (2023)
- [How Auditing Methodologies Can Impact Our Understanding of YouTube's Recommendation Systems](https://mlsanity.com/paper/2303.03445/) (2023)

## Built on by (most cited first, 5 of 5)

- [Correlated Errors in Large Language Models](https://mlsanity.com/paper/2506.07962/) (2025)
- [SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction](https://mlsanity.com/paper/2508.05691/) (2025)
- [Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives](https://mlsanity.com/paper/2509.05627/) (2025)
- [PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Research](https://mlsanity.com/paper/2508.09232/) (2025)
- [AISPA: User-Centric System Prompt Auditing for Large Language Model Applications](https://mlsanity.com/paper/2607.28617/) (2026)

## Abstract (arXiv)

For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the `practice' of algorithmic auditing thereby presaging how this `ecosystem' will develop. In this paper, we systematically review the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. Who is likely to occupy this space? What are some political and ethical tensions that are likely to arise? How are the mandates of `independent auditing' or `the evaluation of the societal context of an algorithmic function' likely to play out in practice? By shaping the picture of the emerging political economy of algorithmic auditing, we draw attention to strategies and cultures of traditional auditors that risk eroding important regulatory pillars of the DSA and the OSA. Importantly, we warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors and/or diluted within a complex web of (sub-)contractual arrangements, diverse portfolios, and tight timelines.