The literature identifies consistent concerns about risks
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
The literature identifies consistent concerns about risks associated with AI, including risk of algorithmic bias and lack of transparency and accountability. The study provides direction regarding priority gaps that need to be addressed for
Evidence profile
Sourced from the stated research gap and future-work section and limitations of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 15 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- From Automation to Augmentation: A Bibliometric and Thematic Review of Artificial Intelligence in Human Resource Management (2026) · International Review of Management and Marketing · doi
The literature identifies consistent concerns about risks associated with AI, including risk of algorithmic bias and lack of transparency and accountability. The study provides direction regarding priority gaps that need to be addressed for the effective and ethically appropriate application of AI in HRM.
generalstated research gapKeywords: literature identifies consistent concerns about risks associated including - Guest editorial: Artificial intelligence (AI) in the world of work: bibliometric insights and mapping opportunities and challenges (2025) · Personnel Review · cited 15× · doi
The paper suggests that future research should focus on the practical implications of AI adoption in HRM. The study highlights the need for more research on employee-centric outcomes of AI adoption and assimilation in the field. The authors suggest that future research should explore the challenges and opportunities of AI in HRM.
generalfuture-work sectionKeywords: paper suggests future research focus practical implications adoption - AI-ENABLED TRANSFORMATION OF HR: A CONCEPTUAL REVIEW OF TRAINING AND TALENT MANAGEMENT (2026) · Sohar University Journal of Sustainable Business · doi
2 Future Research Directions Future research should focus on empirical validation of the proposed model using quantitative techniques such as Structural Equation Modelling (SEM) or SmartPLS to test relationships between AI capabilities and HR outcomes. Further research is necessary to examine ethical issues, including algorithmic discrimination, system openness, 73 Dash et al.
generallimitationsevidence 5/5Keywords: future directions focus empirical validation proposed model using quantitative techniques structural equation modelling smartpls test
Questions about this gap
Explore this gap further
Run this gap as a query across open scholarly engines for the latest related literature.
Working on this gap? Review it with us.
Science AI Journal reviews manuscripts in one pass with 8 specialised AI agents calibrated on 69,000+ real peer reviews.
Tools for your next paper
Related gaps in Computer Science
- Agent skills -- structured, reusable knowledge artifactsAgent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet t…
- Conventional remote sensing classification methodsConventional remote sensing classification methods are often limited by inadequate feature representation and weak discriminative capability…
- The challenge of gaps in satellite inputs due to orbitalThe challenge of gaps in satellite inputs due to orbital sampling and cloud contamination. The challenge of uncertainty in input fields, suc…
- Comprehensive meta-analyses examining the effectComprehensive meta-analyses examining the effect of resistance training on functional decline in older adults with dementia. Prior studies h…