When using AI, how can we ensure the scientific nature and credibility of research results?

Ensuring the scientific nature and credibility of research utilizing artificial intelligence (AI) requires adherence to rigorous methodological principles and transparent reporting, which is demonstrably feasible. Maintaining high standards involves specific adaptations to traditional research practices to address AI's unique characteristics. Key principles include explicit problem formulation aligning AI capabilities with research objectives, meticulous data management protocols ensuring quality and representative sampling, and thorough validation using established metrics on independent holdout datasets. Methodological transparency necessitates documenting the AI model architecture, training processes, hyperparameter settings, and data provenance in detail. Rigorous bias assessment and mitigation strategies must be integrated throughout the AI lifecycle, and human expert oversight remains critical for interpreting results, contextualizing findings, and identifying potential limitations or artifacts introduced by the AI tools. Implementation requires clearly defining the problem and selecting appropriate AI techniques; carefully curating, preprocessing, and documenting training and validation data; rigorously training and validating models using robust metrics; critically analyzing results, including uncertainty estimates and error analysis; documenting all tools, code, and parameters comprehensively; and integrating expert review throughout the process to ensure conclusions are scientifically sound and supported by the evidence. This structured approach enhances reproducibility and fosters trust in AI-derived research outcomes.