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    Using AI in evidence synthesis safely

    28 min
    |
    |
    6 апр. 2026 г.
    AIScienceTechnology

    Manual research reviews often miss key data. Learn how the new RAISE framework ensures AI transparency while keeping human researchers accountable.

    Using AI in evidence synthesis safely

    Лучшая цитата из Using AI in evidence synthesis safely

    “

    The technology might be disruptive, but our standards are non-negotiable. You can use AI, but the human researcher stays 100% accountable for every single finding.

    ”

    Этот аудиоурок был создан участником сообщества BeFreed

    Вопрос для ввода

    Position Statement on Artificial Intelligence (AI) Use in Evidence Synthesis Across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025 https://onlinelibrary.wiley.com/doi/10.1002/cl2.70074

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    Direct source: onlinelibrary.wiley.com
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    https://onlinelibrary.wiley.com/doi/10.1002/cl2.70074

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    "Feels effortless compared to reading. I’ve finished 6 books this month already."

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    "Makes me feel smarter every time before going to work"

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    "I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

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    "Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

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    "Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

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    "Reading used to feel like a chore. Now it’s just part of my lifestyle."

    @Erin, Investment Banking Associate , NYC
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    "Feels effortless compared to reading. I’ve finished 6 books this month already."

    @djmikemoore
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    "BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

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    "The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

    @Leo, Law Student, UPenn
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    "Makes me feel smarter every time before going to work"

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    Ключевые выводы

    1

    The AI Guardrails for Research

    0:00

    Nia: I was just thinking about how much pressure researchers are under to stay current, especially when you realize that in rapid reviews, a single person screening abstracts has about a 13% risk of accidentally missing a relevant study. It’s a huge margin for error!

    0:15

    Eli: It really is, and that’s exactly why the big players—Cochrane, Campbell, JBI, and the CEE—just released a unified position statement on using AI to help close those gaps. The headline is clear: you can use AI, but the human researcher stays 100% accountable for every single finding.

    0:34

    Nia: Right, so it’s not about handing the keys over to a "black box" algorithm. It’s about using these tools without compromising rigor or integrity.

    0:43

    Eli: Exactly. They’re backing a framework called RAISE to ensure that if a machine makes a judgment, it’s fully transparent. Let’s explore how these new requirements for disclosure and human oversight actually work in practice.

    2

    Defining the Accountability Frontier

    3

    Navigating the Transparency Mandate

    4

    The Developer's New Responsibility

    5

    Balancing Trade-offs in the Research Process

    6

    Human Oversight as the Ultimate Filter

    7

    The Global Infrastructure of Evidence

    8

    A Practical Playbook for the Researcher

    9

    Closing Reflection and Moving Forward

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