15 Years of Algorithmic Fairness -- Scoping Review of Interdisciplinary Developments in the Field

This paper presents a scoping review of algorithmic fairness research over the past fifteen years, utilising a dataset sourced from Web of Science, HEIN Online, FAccT and AIES proceedings. All articles come from the computer science and legal field and focus on AI algorithms with potential discriminatory effects on population groups. Each article is annotated based on their discussed technology, demographic focus, application domain and geographical context. Our analysis reveals a growing trend towards specificity in addressed domains, approaches, and demographics, though a substantial portion of contributions remains generic. Specialised discussions often concentrate on gender- or race-based discrimination in classification tasks. Regarding the geographical context of research, the focus is overwhelming on North America and Europe (Global North Countries), with limited representation from other regions. This raises concerns about overlooking other types of AI applications, their adverse effects on different types of population groups, and the cultural considerations necessary for addressing these problems. With the help of some highlighted works, we advocate why a wider range of topics must be discussed and why domain-, technological, diverse geographical and demographic-specific approaches are needed. This paper also explores the interdisciplinary nature of algorithmic fairness research in law and computer science to gain insight into how researchers from these fields approach the topic independently or in collaboration. By examining this, we can better understand the unique contributions that both disciplines can bring.