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Frequently Asked Questions (FAQ)

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Do you have any questions? Here, you¡¯ll find answers to some of the most common questions about ImpactAI, from how it works to when it will be available.

If there is anything we haven¡¯t covered here, please contact us at impactai@worldbank.org, and we¡¯ll be happy to assist you!

 

General Information

ImpactAI uses customized large language models (LLMs) and is powered by a structured knowledge database of validated research studies. It retrieves relevant information from these studies, provides summaries of research findings, visualizes the impact of different policy options listed in the summaries, provides links to the research sources, and recommends follow-up prompts to support further exploration.

ImpactAI¡¯s differentiating factor is our curated knowledge database of research studies. By feeding quantitative, structured information on policy interventions, outcomes, standardized effect sizes, and study design, ImpactAI significantly reduces the risk of hallucinations that generic generative AI tools exhibit.

ImpactAI is built for development practitioners, policymakers, researchers, international organizations, and other stakeholders who need accurate, research-backed insights to inform their policy decisions.

ImpactAI allows users to quickly access and compare the impact of different policy interventions. For example, if you are working to reduce food insecurity, it provides summaries of studies that show the effectiveness of various approaches, helping you allocate resources more effectively.

Availability and Access

ImpactAI is expected to be publicly available in Spring 2025.

ImpactAI will be open-source and free to access, available to all users, including private firms and nonprofits. You can sign up for updates on the product launch .

Yes, ImpactAI will include papers that are subscription-based, in addition to open-access studies. Please note that some sources may require a login or membership to view in full.

Research and Data Sources

No, ImpactAI includes research beyond top academic journals. ImpactAI also aims to cover a wide range of research, not limited to top journals, though the quality of included research is prioritized.

The initial phase of ImpactAI prioritizes randomized controlled trials (RCTs) because they are often considered the gold standard in evaluating interventions. However, future versions will include other forms of peer-reviewed program evaluation, including natural experiments and quasi-experimental designs.

Currently, ImpactAI covers only development economics research. However, the tool is designed to evolve, and future versions may include research from other disciplines.

Yes, our team tags and validates the quality of each paper to ensure it meets our rigorous inclusion criteria. We incorporate quality indicators like citation counts, publication in peer-reviewed journals or reputable working paper series, and the robustness of the research¡¯s methodology to ensure high-quality insights.

ImpactAI's taxonomies are derived from various existing, established taxonomies to ensure accuracy and standardization to users.

Poor language in research studies could impact how information is extracted and populated in our database. Although diverse data may affect overall performance, our current model is already robust. We are developing specific solutions to address challenging cases.

Yes, we are open to new research sources. All research must be peer-reviewed before submission for consideration and will be subject to our terms and conditions. Submission does not guarantee inclusion in the database.

Technology and Features

Yes, users are provided with links to access the original research sources. Some sources may require login or membership with the publisher or data partner.

Yes, ImpactAI provides interactive graphs that visualize the impact of different policy interventions and directly link to the data points' respective sources.

Yes, our team developed EconBERTa, which informed the development of ImpactAI.

The language models are fine-tuned on a structured knowledge base that includes validated research studies. Human annotators ensure that the model is trained on relevant, high-quality data, and we use our own curated validation datasets to measure our models¡¯ performance.

Biases primarily affect summarization tasks. To mitigate this, we restrict the model's access to our validated database, impose rigorous guidelines, and implement structured prompting frameworks. These measures help reduce hallucinations and limit the spread of bias. All outputs are reviewed by both LLM-based evaluators and human reviewers to maintain accuracy.

ImpactAI guarantees accuracy by relying on a structured knowledge base comprised of validated, peer-reviewed research studies. This prevents the generation of unverified content ("hallucinations") and ensures that outputs are both accurate and reliable.

We can ensure such high-quality standards due to a three-step process:

  1. Verified Evidence Database: We curate a database of evidence-based impact evaluations using our advanced information extraction system, overseen by a team of annotators who manually verify the information.
  2. Natural Language Understanding (NLU): Our NLU module is specifically designed to recognize key concepts from impact evaluation research. It queries our database for precise, context-relevant information.
  3. Summarization: A fine-tuned LLM generates concise, evidence-based answers, strictly constrained to the verified database, preventing hallucinations.

This process ensures ImpactAI consistently delivers reliable, evidence-driven insights to researchers and policymakers.

Future Development

Currently, ImpactAI only supports research studies in English. Future updates are expected to include studies in other languages as the tool evolves.

Yes, ImpactAI will include an API for users who want to dig deeper and use our research data for their analyses.

While the initial version of ImpactAI does not have this feature, we are working on developing predictive models. There is already some research in this space using off-the-shelf LLMs, but we expect our models to perform better since our model is fine-tuned on actual impact evidence.

By providing users with the highest-quality research findings, ImpactAI supports understanding the current research space and can inspire new research questions.

Currently, ImpactAI focuses on providing accurate, quantitative insights. Future iterations of the tool are expected to incorporate more qualitative insights to help users understand the context and mechanisms behind successful interventions.

Getting Involved and Staying Updated

You can sign up for updates on ImpactAI . This will keep you informed about the tool¡¯s launch, new features, and additional research included in our structured database.

We always welcome user feedback to help us improve ImpactAI. You can participate in upcoming beta testing by signing up , or submit feedback and suggest new features by contacting us at impactai@worldbank.org.

ImpactAI Product Showcase

Key Features

  • Example of ImpactAI's summary text output on a blue gradient background
    Generative text summarization of key causal relationships and findings
  • Example of ImpactAI's interactive plot outputs on a blue gradient background
    Intervention and outcomes visualized on a forest plot
  • Example of ImpactAI's interactive source reference output on a blue gradient background
    Direct links to accessible evidence-backed sources

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