  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.
