Law and the Emerging Political Economy of Algorithmic Audits
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 136 references that are in the map. First posted on arXiv 3 Apr 2024.
Builds on
21 of 136 references.
References · 21
- 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
- 2020Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditingcited by 380
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
- 2020Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditingcited by 89
- 2021Demystifying the Draft EU Artificial Intelligence Actcited by 82
- 2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
- 2023Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystemcited by 49
- 2021Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviorscited by 47
- 2023Understanding accountability in algorithmic supply chainscited by 39
- 2020Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practicescited by 38
- 2022Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethicscited by 28
- 2021Problematic Machine Behavior: A Systematic Literature Review of Algorithm Auditscited by 20
9 more
- 2024Auditing Work: Exploring the New York City algorithmic bias audit regimecited by 18
- 2023Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UKcited by 13
- 2023Auditing Cross-Cultural Consistency of Human-Annotated Labels for Recommendation Systemscited by 9
- 2023Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediariescited by 9
- 2021Bugs in our Pockets: The Risks of Client-Side Scanningcited by 5
- 2022Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interestcited by 4
- 2023Auditing Recommender Systems -- Putting the DSA into practice with a risk-scenario-based approachcited by 4
- 2023MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualitiescited by 2
- 2023How Auditing Methodologies Can Impact Our Understanding of YouTube's Recommendation Systemscited by 1
Built on by
Citing papers per year: 2025: 4; 2026: 1.
5 papers.
- 2025Correlated Errors in Large Language Modelscited by 42
- 2025SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstructioncited by 4
- 2025Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternativescited by 2
- 2025PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Researchcited by 1
- 2026AISPA: User-Centric System Prompt Auditing for Large Language Model Applicationscited by 0
- 2026AISPA: User-Centric System Prompt Auditing for Large Language Model Applications30 Jul 2026
- 2025Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternatives6 Sep 2025
- 2025PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Research12 Aug 2025
- 2025SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction6 Aug 2025
- 2025Correlated Errors in Large Language Models9 Jun 2025