Five lessons from building evidence-strength scoring into an AI policy tool
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This article discusses five key lessons learned from building evidence-strength scoring into Policy Atlas, an AI tool designed to support policy decisions. The first lesson emphasizes that relevant evidence is not always strong evidence — different types of evidence (systematic reviews, observational studies, policy reports) vary in methodological rigor, transparency, and trustworthiness for causal claims. The tool aims to help users understand what kind of evidence they're looking at and how much weight to place on it.
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bskyFive lessons from building evidence-strength scoring into an AI policy toolnesta.org.ukKey quotes
· 4 pulledThe evidence retrieved by Policy Atlas does not all have the same methodological strength.
A systematic review, a single observational study and a policy report may all be relevant to the same question, but should not be interpreted in the same way.
They differ in how evidence is gathered, how transparent the methodology is, and how much they can be trusted in supporting a causal claim.
To support policy decisions, Policy Atlas needs to help users understand the kind of evidence they are looking at, how much weight to place on it and how much confidence to place in the assessment.
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