This study explores the impact of technological innovations on audit transparency, objectivity, and assurance. The study employs a systematic literature review methodology, analyzing a wide range of scholarly articles, research papers, and reports to synthesize the findings. The methodology involved identifying keywords, conducting comprehensive searches in academic databases, and evaluating the selected literature. The study identifies key themes on how technological innovations impact audit practices through analysis of the literature. The impacts of technology include enhanced audit transparency through improved documentation capabilities, real-time reporting, and increased stakeholder engagement. Technological advancements bolster audit objectivity by automating repetitive tasks, facilitating advanced data analysis, and promoting standardized audit procedures. However, the analysis highlighted challenges associated with the use of technology in audits including complex technology implementation and the potential for biases. This research study contributes to the existing body of knowledge by consolidating relevant research and insights on the subject matter.

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.