The field of recruitment and hiring is undergoing significant transformations with the integration of artificial intelligence (AI) and machine learning (ML) technologies. Recent studies have focused on evaluating the effectiveness and fairness of AI-powered recruitment tools, with a particular emphasis on large language models (LLMs) and their ability to screen resumes, match candidates to job openings, and predict candidate success. One of the primary directions in this field is the development of more sophisticated and fair AI systems that can accurately assess candidate competence while minimizing biases. Researchers have proposed various methods to achieve this, including the use of hierarchical job classification, similarity graph integration, and multi-layer LLM-based robotic process automation. Another crucial area of research is the mitigation of structural inequalities and biases in AI-driven recruitment systems. This involves the development of theories such as secondary bounded rationality, which explains how AI systems can perpetuate and amplify existing biases, as well as the proposal of mitigation strategies like counterfactual fairness testing and capital-aware auditing. Noteworthy papers in this area include ones that introduce innovative applicant tracking systems enhanced by robotic process automation frameworks, and studies that investigate the competence and biases of AI-powered resume screening tools, highlighting the importance of auditing these tools for both fairness and effectiveness. Furthermore, the creation of public benchmarks and evaluation campaigns, such as TalentCLEF 2025, is vital for advancing the field by providing a platform for the development and comparison of reliable and fair models for skill and job title intelligence.