WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms -- A Systematic Literature Review
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916632226430976 |
|---|---|
| author | Urman, Aleksandra Makhortykh, Mykola Hannak, Aniko |
| author_facet | Urman, Aleksandra Makhortykh, Mykola Hannak, Aniko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11194 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms -- A Systematic Literature Review Urman, Aleksandra Makhortykh, Mykola Hannak, Aniko Human-Computer Interaction 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. |
| title | WEIRD Audits? Research Trends, Linguistic and Geographical Disparities in the Algorithm Audits of Online Platforms -- A Systematic Literature Review |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2401.11194 |