Artificial intelligence and evidence-informed policy: emerging challenges and opportunities: discussion paper
This discussion paper by the World Health Organization examines the intersection of artificial intelligence and evidence-informed policy-making in the health sector. It outlines how AI supports various stages of the policy cycle—such as problem identification, design, and implementation—through predictive modelling and enhanced data integration. The document also analyzes key risks like bias, opacity, and regulatory gaps, while proposing practical considerations for responsible implementation and risk-based regulation.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This document is intended for policy-makers, regulators, and health stakeholders seeking to understand how to leverage AI to strengthen policy processes and improve health outcomes.
What the document contains
The document addresses the intersection of artificial intelligence and evidence-informed policy in health, examining how AI supports the policy cycle and reviewing associated risks, governance frameworks, and practical considerations for responsible implementation.
The text does not provide binding legal regulations, specific technical compliance mandates, or a detailed regulatory enforcement mechanism.
- Artificial intelligence increasingly shapes evidence-informed policy-making in health by enabling faster analysis and synthesis of diverse data sources.
- AI tools can support problem identification, policy design, and implementation through predictive modelling and scenario simulation.
- AI systems are intended to augment rather than replace human judgement in decision-making contexts.
- Integration of AI introduces significant challenges including bias, opacity, equity concerns, and regulatory gaps.
- Responsible implementation requires human oversight, multidisciplinary collaboration, living evidence approaches, and risk-based regulation.
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
0/5Clear responsibility for AI outcomes
Overview
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
including bias, opacity, equity concerns, data governance and regulatory gaps
Overview
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
Overview
screening score · 2026-10-23
Human Oversight
3/5Humans remain in control of consequential decisions
It emphasizes that AI augments rather than replaces human judgement, while highlighting its potential to expand the evidence base
Overview
screening score · 2026-10-23
Privacy
1/5Protection of personal data and private life
data governance and regulatory gaps
Overview
screening score · 2026-10-23
Transparency
2/5Openness about how AI systems work and are used
The paper analyzes key risks and challenges associated with AI integration, including bias, opacity, equity concerns
Overview
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Overview
screening score · 2026-10-23
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
The paper analyzes key risks and challenges associated with AI integration, including bias, opacity, equity concerns, data governance and regulatory gaps
Overview
screening score · 2026-10-23