This paper aims to structure the research addressing the application of artificial intelligence in HRM, focusing on the recruitment and selection process (RSP). The aim is to identify which RSP stages have been the focus of AI and which algorithms are used. A taxonomy of AI research was used. The analysis combines a literature review and computational analysis, based on a qualitative content analysis, and computational analysis, using NLP. The initial 4,579 studies were sourced from three databases and narrowed down to a total of 502 in a qualitative and iterative process. The computational analysis was performed with Python. While AI was introduced relatively late in HRM research, since 2009, most studies have been categorised under the stages “assessment & selection” and “processing incoming applications” in the RSP. They mainly focus on ranking candidates and analysing resumes / CVs. The predominately used algorithms were found in the field of NLP and machine learning, with BERT serving as a bridge between both categories. The computational analysis highlighted the importance of ethics in AI research, thereby contributing to the expansion of the general AI taxonomy used. This paper contributes to expanding the general AI taxonomy by incorporating an ethical perspective. Additionally, it highlights which algorithms are predominately represented in research. Consequently, companies should prioritise implementing these algorithms, while research should aim to explore and test alternative options. This study is unique in its approach. To our knowledge, it is the first to comprehensively analyse the use of algorithms in RSP.