Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research

TL;DR

This paper introduces AVA, a trustworthy generative AI platform for policy research that emphasizes evidence-based responses, epistemic humility, and operational transparency, tested through extensive real-world evaluation.

Contribution

It presents AVA, a novel GenAI system built on curated sources with mechanisms for citation verification and abstention, along with design guidelines for trustworthy AI in policy contexts.

Findings

Sustained engagement saved 2.4-3.9 hours weekly for users.

Participants used AVA as a specialized evidence engine.

Trust was enhanced through provenance and citation anchoring.

Abstract

General-purpose LLMs pose misinformation risks for development and policy experts, lacking epistemic humility for verifiable outputs. We present AVA (AI + Verified Analysis), a GenAI platform built on a curated library of over 4,000 World Bank Reports with multilingual capabilities. AVA's multi-agent pipeline enables users to query and receive evidence-based syntheses. It operationalizes epistemic humility through two mechanisms: citation verifiability (tracing claims to sources) and reasoned abstention (declining unsupported queries with justification and redirection). We conducted an in-the-wild evaluation with over 2,200 individuals from heterogeneous organisations and roles in 116 countries, via log analysis, surveys, and 20 interviews. Difference-in-Differences estimates associate sustained engagement with 2.4-3.9 hours saved weekly. Qualitatively, participants used AVA as a specialized "evidence engine"; reasoned abstention clarified scope boundaries, and trust was calibrated through institutional provenance and page-anchored citations. We contribute design guidelines for specialized AI and articulate a vision for "ecosystem-aware" Humble AI.