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.

翻译：在线平台对复杂算法系统日益增长的依赖催生了对算法审计的迫切需求——这是一种评估这些系统功能性和社会影响的研究方法。本文系统性地梳理了算法审计研究，识别了其在方法路径、作者地理分布、平台选择、语言覆盖、地域范围及群体属性聚焦等方面的趋势。我们发现了研究重点显著偏向西方语境（尤其是美国）以及英语数据占比畸高的证据。此外，分析表明算法审计研究倾向于聚焦少量群体属性，且常以简化方式操作化，这可能导致算法偏见与歧视中更细微层面的被遮蔽。通过本次综述，我们旨在更清晰地理解算法审计领域的当前状态，并识别出为实现更具包容性与代表性的研究格局而亟待填补的空白。