Navigating the Integration of AI: The Pew Research Center Approach

Originally published on the Decoded Blog | Data Science Authored by Claudia Deane | September 28, 2026

Pew Research Center is currently evaluating artificial intelligence through a multifaceted lens. As an organization dedicated to social science and public transparency, we view AI not as a replacement, but as a potential addition to our professional toolkit.

Our strategy is defined by three primary objectives:

1. Innovation: Exploring how AI can enhance our research capabilities.
2. Analysis: Investigating the public's perception and reaction to AI's evolution.
3. Integrity: Maintaining a slow, deliberate pace to ensure that methodological rigor and accuracy are never compromised.

Our Core Mandate: We remain steadfast in our commitment to people-centered research. This means that the data we produce is derived from actual human beings, not synthetic entities.

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The Boundary: Where AI is NOT Used

To maintain the gold standard of public opinion research, there are strict boundaries we will not cross. We believe that the most critical intellectual decisions must remain human.

• Survey Participation: We do not use AI to simulate respondents.
• Topic Selection: AI does not decide which research questions are analytically significant.
• Final Accountability: Machines do not hold responsibility for the accuracy of our findings.
• Privacy Violations: We never input personally identifying information (PII) into public AI systems.
• Synthetic Imagery: All photographs used in our reports are authentic images of real people.

For our probability-based panels, we maintain a sample size of approximately: $N \approx 10,000 \text{ adults selected randomly across the U.S.}$

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The Application: Where AI IS Used

While we avoid AI in the "soul" of our research, we leverage it for technical efficiency and data organization.

Technical & Analytical Support

Our engineers and researchers utilize AI for the "heavy lifting" of coding and data cleaning.

Workflow Integration

The following diagram illustrates how AI fits into our production pipeline:

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Implementation Standards

To ensure we are using these tools ethically, we follow a strict internal checklist:

• Ensure no PII is exposed to open LLMs.
• Verify that AI-generated code is functionally accurate.
• Confirm that AI-assisted copy editing hasn't altered the original meaning.
• Validate that derivative products (e.g., tweets) reflect the original human-authored research.

For example, when a researcher needs to automate a data cleaning task, they might use an AI assistant to generate a Python snippet:

# Example of AI-assisted data cleaning for survey responses
def clean_survey_text(text):
    # AI helps suggest regex patterns for cleaning
    import re
    return re.sub(r'\s+', ' ', text).strip()

Our Commitment to Transparency

We believe in being open about our methods. If AI plays a meaningful role in the production of a specific report—such as the categorization of open-ended survey text—we will explicitly disclose this in the Methodology section of that report.

Should our use of AI evolve to materially change how our external products are created, we pledge to:

1. Re-evaluate our disclosure protocols.
2. Update this public guidance accordingly.

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We welcome your feedback, concerns, and hopes regarding our use of these technologies.

Note: This article serves as an update to a previous post from August.