Mapping the Field of Algorithm Auditing: A Systematic Literature Review Identifying Research Trends, Linguistic and Geographical Disparities

Urman, Aleksandra; Makhortykh, Mykola; Hannak, Aniko (20 January 2024). Mapping the Field of Algorithm Auditing: A Systematic Literature Review Identifying Research Trends, Linguistic and Geographical Disparities (arXiv). Cornell University 10.48550/arXiv.2401.11194

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The increasing reliance on complex algorithmic systems by online platforms has sparked a growing need for algorithm auditing, a research methodology evaluating these systems' functionality and societal impact. In this paper, we systematically review 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 auditing research. We present evidence of a significant skew of research focus toward Western contexts, particularly the US, and a disproportionate reliance on English language data. Additionally, our analysis indicates a tendency in algorithm auditing studies 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. By conducting this review, we aim to provide a clearer understanding of the current state of the algorithm auditing field and identify gaps that need to be addressed for a more inclusive and representative research landscape.

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