How can bias be avoided when using AI-assisted literature screening?

Bias mitigation during AI-assisted literature screening is achievable through rigorous methodological design and critical human oversight. Automated tools must augment, not replace, expert judgment to ensure equitable and valid study selection. Key principles involve utilizing transparent, validated AI algorithms designed for systematic review screening. Training data must be comprehensive and representative of relevant populations and study types to minimize algorithmic bias. Explicit predefined inclusion/exclusion criteria must be consistently applied, both in training the AI and during human validation stages. Critical attention to potential biases related to publication source, language, or study design within the training corpus and screened results is essential. Continuous quality checks, like interrater reliability assessments and reflective sampling validation, between AI output and human screeners are mandatory safeguards. Implementation requires proactive collaboration. Begin by rigorously developing and preregistering the screening protocol, including detailed criteria. Select AI tools proven in systematic reviews and thoroughly train them using balanced, relevant datasets screened by domain experts. Configure software according to the protocol. Screen initial results iteratively: run AI screening, then conduct rigorous dual-human screening on a significant sample or the entire set. Compare results, analyze discrepancies to identify AI bias patterns, refine criteria/training as needed, and repeat until consistency is achieved. Finally, manually review all uncertain records flagged by the AI. This process ensures efficiency while leveraging human contextual understanding for final inclusion decisions, maximizing validity and reducing systematic error.