# WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms -- A Systematic Literature Review

arXiv: https://arxiv.org/abs/2401.11194 · HTML: https://mlsanity.com/paper/2401.11194/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing](https://mlsanity.com/paper/2410.04772/) (2024)
- [The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok](https://mlsanity.com/paper/2603.05653/) (2026)
- [Now You See Me: Designing Responsible AI Dashboards for Early-Stage Health Innovation](https://mlsanity.com/paper/2602.23378/) (2026)

## Abstract (arXiv)

The increasing reliance on complex algorithmic systems by online platforms has sparked a growing need for algorithm auditing, a methodology evaluating these systems' functionality and impact. In this paper, we systematically review 176 peer-reviewed online platform-focused algorithm auditing studies and identify trends in their methodological approaches, the geographic distribution of authors, and the selection of platforms, languages, geographies, and group-based attributes in the focus of the reviewed research. We find a significant skew of research focus towards few online platforms, Western contexts, particularly the US, and English language data. Additionally, our analysis indicates a tendency to focus on a narrow set of group-based attributes, often operationalized in simplified ways, which might obscure more nuanced aspects of algorithmic bias and discrimination. We provide a clearer understanding of the current state of the online platform-focused algorithm auditing and identify gaps to be addressed for a more inclusive and representative research landscape.