Who is ultimately responsible for the findings if a researcher uses AI to conduct a study?

The human researcher remains 100% accountable for every finding, method, and piece of content generated, even if an AI tool was used. According to the joint position statement from Cochrane, Campbell, JBI, and the CEE, researchers cannot blame an algorithm for errors or "hallucinations." This "human-in-the-loop" model requires authors to co-sign every output and be prepared to answer for the integrity of the data, acting as the final filter for all machine-generated judgments.

What is the RAISE framework and how does it impact research?

RAISE stands for the Responsible use of AI in evidence SynthEsis. It is a systemic framework designed to ensure transparency and rigor when advanced technologies are used in research. It maps out specific responsibilities for the entire ecosystem, including tool developers, methodologists, and publishers. For researchers, it mandates clear disclosure of how AI was used; for developers, it requires "giving the receipts" by providing public information about training data, potential biases, and system limitations.

When is it necessary to disclose the use of AI in a research paper?

Disclosure is required whenever AI makes or suggests a scientific judgment, such as screening abstracts, extracting data, or synthesizing qualitative findings. Low-risk tasks, such as using basic scripts for spelling and grammar checks, generally do not need to be listed. However, for more substantive tasks, researchers must provide a "biography" of the AI usage, including the tool's name, version, platform, and a justification for why that specific tool was appropriate for the research question.

How should researchers handle the "black box" problem of AI algorithms?

To counter the "black box" nature of AI—where the logic between input and output is hidden—researchers must practice "active skepticism." This involves validating the tool through piloting or calibration, such as comparing AI results against human results in a small sample. Furthermore, researchers are encouraged to make their "prompts," code, and datasets publicly available so that the logic used to reach a conclusion is visible and can be critiqued by the wider scientific community.

What are the trade-offs researchers must consider before using AI?

Researchers must weigh the gains in speed and efficiency against the potential risks of error and the "upfront investment" of time required to validate the tool. While AI can mitigate human error—such as the 13% risk of a single human missing a relevant study during screening—it can also introduce systemic biases based on its training data. Additionally, the framework encourages researchers to consider the environmental and social costs, such as the high energy consumption required to run large language models.

Using AI in evidence synthesis safely

28 min

6 de abr. de 2026

AI Science Technology

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

Melhor citação de 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.

Esta aula em áudio foi criada por um membro da comunidade BeFreed

Pergunta de entrada

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

Vozes dos apresentadores

Nia

Eli

Estilo de aprendizagem

Profundo

Fontes de conhecimento

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

Perguntas frequentes

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

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@djmikemoore

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

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Using AI in evidence synthesis safely

28 min

6 de abr. de 2026

AI Science Technology

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

Melhor citação de 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.

Pontos-chave

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.

Defining the Accountability Frontier

Navigating the Transparency Mandate

The Developer's New Responsibility

Balancing Trade-offs in the Research Process

Human Oversight as the Ultimate Filter

The Global Infrastructure of Evidence

A Practical Playbook for the Researcher

Closing Reflection and Moving Forward

Esta aula em áudio foi criada por um membro da comunidade BeFreed

Pergunta de entrada

Vozes dos apresentadores

Nia

Eli

Estilo de aprendizagem

Profundo

Fontes de conhecimento

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

Perguntas frequentes

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Criado por ex-alunos da Universidade de Columbia em San Francisco

BeFreed Reúne Uma Comunidade Global De 1,000,000 Mentes Curiosas

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"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

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

@Chloe, Solo founder, LA

117

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

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

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@Moemenn

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

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

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@SofiaP

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@OojasSalunke

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"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

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