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1<title>GenAI: Substitute or Complement in Evidence Synthesis: A discrete choice experiment with Italian policymakers. First draft available soon. | Sergiu Burlacu</title></head><body id=top data-spy=scroll data-offset=70 data-target=#TableOfContents class=page-wrapper>
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1<aside class=search-modal id=search><div class=container><section class=search-header><div class="row no-gutters justify-content-between mb-3"><div class=col-6><h1>Search</h1></div><div class="col-6 col-search-close"><a class=js-search href=# aria-label=Close><i class="fas fa-times-circle text-muted" aria-hidden=true></i></a></div></div><div id=search-box><input name=q id=search-query placeholder=Search... autocapitalize=off autocomplete=off autocorrect=off spellcheck=false type=search class=form-control aria-label=Search...></div></section><section class=section-search-results><div id=search-hits></div></section></div></aside><div class=page-header><nav class="navbar navbar-expand-lg navbar-light compensate-for-scrollbar" id=navbar-main><div class=container-xl><div class="d-none d-lg-inline-flex"><a class=navbar-brand href=/>Sergiu Burlacu</a></div><button type=button class=navbar-toggler data-toggle=collapse data-target=#navbar-content aria-controls=navbar aria-expanded=false aria-label="Toggle navigation"> 2<span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=/>Sergiu Burlacu</a></div><div class="navbar-collapse main-menu-item collapse justify-content-start" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=/#about><span>Home</span></a></li><li class=nav-item><a class=nav-link href=/#publications><span>Research</span></a></li><li class=nav-item><a class=nav-link href=/#contact><span>Contact</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"><li class=nav-item><a class="nav-link js-search" href=# aria-label=Search><i class="fas fa-search" aria-hidden=true></i></a></li><li class="nav-item dropdown theme-dropdown"><a href=# class=nav-link data-toggle=dropdown aria-haspopup=true aria-label="Display preferences"><i class="fas fa-moon" aria-hidden=true></i></a><div class=dropdown-menu><a href=# class="dropdown-item js-set-theme-light"><span>Light</span></a> 3<a href=# class="dropdown-item js-set-theme-dark"><span>Dark</span></a> 4<a href=# class="dropdown-item js-set-theme-auto"><span>Automatic</span></a></div></li></ul></div></nav></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>GenAI: Substitute or Complement in Evidence Synthesis: A discrete choice experiment with Italian policymakers. First draft available soon.</h1><div class=article-metadata><div><span>Martina Bazzoli</span>, <span>Sergiu Burlacu</span>, <span>Dominique Cappellett</span>, <span>Sonia Marzadro</span>, <span>Iunio Quarto Russo</span>, <span>Alessio Tommelleri</span></div><span class=article-date>January 2026</span></div><div class="btn-links mb-3"><a class="btn btn-outline-primary btn-page-header" href=https://irvapp.fbk.eu/projects/detail/policyaid/ target=_blank rel=noopener>Project</a></div></div><div class=article-container><h3>Abstract</h3><p class=pub-abstract>Credible evidence on the causal effects of public policies is increasingly produced by the academic community, and evidence-based policymaking is increasingly demanded by governments, yet the production of high-quality evidence syntheses remains costly, slow, and often poorly aligned with policymakersâ time constraints. Recent advances in generative artificial intelligence (GenAI) promise efficiency gains in evidence synthesis, but their adoption in policy contexts depends on whether policymakers trust and value AI-generated evidence. This paper studies policymakersâ preferences for the use of GenAI in producing scientific evidence syntheses for policy design. We implement a discrete choice experiment with 813 Italian policymakers, eliciting trade-offs over synthesis production method (expert-produced, AI-generated, or hybrid AI-with-expert-review), cost, and delivery time. In a between-subject design, we additionally vary whether the AI model is described as general-purpose or specifically validated for evidence synthesis. We find strong aversion to fully automated AI-produced syntheses, even when conditional on cost and time savings. By contrast, the hybrid AIâexpert approach is systematically preferred over both expert and AI approaches, conditional on cost and delivery time. Explicit validation of the AI model significantly increases acceptance of AI-assisted synthesis, reducing resistance to automation and increasing preferences for hybrid production. Heterogeneity analyses show that these effects vary with attitudes toward AI but not with gender or institutional role. Overall, the results indicate that policymakers value AI primarily as a complement to human expertise rather than as a substitute, highlighting the importance of task-s
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