How can we ensure the objectivity of the analysis when using AI for literature review?

Objectivity in AI-assisted literature reviews is achievable through careful methodological design, incorporating critical human oversight. It necessitates a commitment to identifying and mitigating embedded biases inherent in both the AI tools and the literature data itself. Crucial principles include employing diverse data sources to counteract coverage bias, carefully selecting and prompting AI tools to minimize interpretive bias, and transparently documenting all AI-involved stages including search terms, tool settings, and preprocessing steps. Human researchers must actively supervise the AI's output, critically evaluating generated summaries, theme categorizations, and relevance assessments for accuracy and potential bias. Rigorous validation of findings against original sources is essential, alongside explicit acknowledgement of the AI's limitations and the specific methodological choices made during the process. Implementation requires iterative steps: researchers must first rigorously screen input data quality; then, design robust, documented AI usage protocols; third, critically analyze AI outputs using predetermined evaluation criteria; fourth, integrate AI outputs with traditional manual analysis through constant comparison; and finally, transparently report the entire AI-enabled workflow, including all limitations and oversight mechanisms, enabling scrutiny and replication.