How can we avoid bias in literature recommendations when using AI?

To mitigate bias in AI-driven literature recommendations, proactive design and diverse data curation are essential. Careful implementation can significantly reduce inherent and algorithmic biases. Key principles include employing comprehensively sourced and representative datasets alongside explicit fairness constraints during model development. Incorporating transparency mechanisms such as explainable AI features allows scrutiny of recommendation pathways, while continuous auditing using counterfactual testing helps identify residual biases. Crucially, human oversight through multidisciplinary expert review ensures cultural and contextual nuances are addressed. Practically, institutions should combine bias-aware auditing of existing systems with active sourcing from underrepresented research areas. Implementation requires building inclusive datasets, applying fairness metrics throughout development, and enabling opt-out mechanisms for users. Rigorous monitoring against inclusivity KPIs maintains equitable knowledge dissemination, fostering innovation while upholding research integrity across diverse scholarly communities.