How Pew Research Center is – and is not – using AI in our work

Billy Odell Tucker-Robinson Founder & Host — Banking With Billy Network • Intelligence Network • Data Science • AI Research • World News
Published: 2026-10-06T00:05:30.371Z • Permanent link

Pew Research Center, a leading non-partisan fact tank, has been making waves in the media landscape by announcing its plans to integrate artificial intelligence (AI) into its research methods. At the forefront of this initiative is Andrew Flanigan, the organization's Director of Research. Flanigan has been vocal about the potential of AI to enhance the accuracy and efficiency of Pew's research, particularly in the realm of social and behavioral sciences. In a statement released earlier this month, Flanigan highlighted the center's decision to leverage AI-powered tools to analyze large datasets and identify patterns that may have gone unnoticed by human researchers. One such project involves the use of natural language processing (NLP) to analyze online forums and social media platforms, providing insights into public opinions and attitudes on various topics.

The Pew Research Center's foray into AI is not without precedent. Other research institutions, such as the Brookings Institution and the Harvard T.H. Chan School of Public Health, have also been exploring the potential of AI in social and behavioral research. However, the Pew Center's approach is notable for its emphasis on transparency and explainability. Flanigan has assured the public that the center's AI-powered research methods will be subject to rigorous scrutiny and peer review, ensuring that the results are not only accurate but also interpretable. As the Pew Center embarks on this new chapter in its research endeavors, it remains to be seen how the integration of AI will impact the quality and validity of its findings.

Meanwhile, some critics have raised concerns about the potential risks of relying on AI-powered research methods. In a statement to the press, Dr. Rachel Sherman, a sociologist at the University of California, Berkeley, expressed reservations about the center's decision to prioritize AI over traditional research methods. "While AI can be a useful tool in certain contexts, it is not a panacea for all research problems," Sherman noted. "We need to be careful not to rely too heavily on machine learning algorithms and forget about the importance of human judgment and critical thinking.

The Pew Research Center's decision to integrate AI into its research methods has significant implications for the social and behavioral sciences. One of the most affected companies is the data analytics firm, Palantir, which has been working closely with the Pew Center to develop AI-powered tools for analyzing large datasets. Palantir's CEO, Alex Karp, has hailed the partnership as a major coup for the company, citing the potential for AI-powered research to revolutionize the field of social science. However, not all researchers are convinced of the benefits of AI-powered research. In a recent interview, Dr. Alan Krueger, a prominent economist at Princeton University, expressed skepticism about the ability of AI to capture the complexities of human behavior.

The Pew Center's decision to prioritize AI-powered research also has broader implications for the research community. Many researchers have expressed concerns about the potential for AI to displace human researchers and undermine the value of traditional research methods. In response, the Pew Center has emphasized the importance of human oversight and interpretation in the AI-powered research process. By striking a balance between machine learning algorithms and human judgment, the Pew Center aims to create a more robust and reliable research methodology.

The Pew Research Center's decision to integrate AI into its research methods is part of a larger trend in the research community. In recent years, there has been a growing recognition of the need for researchers to adapt to the changing landscape of data and analytics. The rise of big data and the increasing availability of machine learning algorithms have created new opportunities for researchers to analyze complex datasets and identify patterns that may have gone unnoticed in the past. However, this trend has also raised concerns about the potential risks of relying too heavily on AI-powered research methods.