Making Good Choices—and Explaining Them
10/05/2026
The survey research field is anything but static. Declining response rates have pushed surveys online, and probability sampling no longer defines our work. Non-probability and blended samples are now a regular part of the landscape, and AI has arrived as a disruptive technology with both promise and peril. The choices for how to conduct public opinion research have never been more numerous.
How we choose should come down to weighing the risks and rewards of each option and then being able to tell the public clearly what we did to get our results.
As chair of the Standards Committee, I see it as our job to give members the information they need to make those choices well. Our work supports one of AAPOR’s strategic pillars, Advancing Best Practices, and two major efforts this year carry it forward: Responsible AI Integration in Survey Research, now available in an interactive format, and the newly revised Code of Professional Ethics and Practices.
The AI Report
Published in May, Responsible AI Integration in Survey Research is a comprehensive guide to when and how artificial intelligence can be used in survey research responsibly, ethically, and transparently. The new interactive version makes it easier to navigate and apply to your own work.
The need for a resource like this is clear. Recently, a purported pollster released findings about upcoming elections based on synthetic respondents, presenting them as if they came from a legitimate survey of real people. We aren’t worried about AAPOR members doing this. But the report is freely available so that anyone who wants to be a better consumer and reporter of survey data has somewhere to turn. Please help us spread the word by sharing it with colleagues, clients, students, and journalists.
The Code Revision
The Code revision advances best practices as well. The 2025/2026 Code Review Committee reviewed the Code and recommended amendments. Member feedback shaped the final version, which the Executive Council approved unanimously and members adopted in June 2026. My thanks to the committee and to everyone who weighed in.
The revisions modernize the Code and strengthen its emphasis on transparency in light of AI and new data collection methods. Terms like “participants,” “poll,” and “survey” are now reserved for data collected from humans, and AI-generated or synthetic data must be clearly identified as such.
The Code’s ethical obligations also now explicitly extend to AI-based research, including privacy and legal responsibilities and the added risk of re-identification. Disclosure expectations have been strengthened across the board: researchers should say plainly what they did, and what they didn’t do, so readers never have to guess. When AI is used, that means stating that it was used, what it did, and what human oversight was in place.
The Code will also no longer follow a fixed five-year review cycle. Instead, it will be reviewed more frequently to keep pace with AI’s rapidly changing role in our field.
Join Us October 8
Want to know what the revised Code means for your work? Join the Standards Committee on October 8 at 1:00 ET for a members-only webinar on the Code revision. We’ll walk through the key changes, explain the thinking behind them, and answer your questions.
We’ll also discuss the role Standards plays in helping researchers be transparent about their methods. That transparency is what makes our choices defensible, whether we use probability, non-probability, or blended samples, or are experimenting with AI. The Code and its companion resources give us a shared language for telling the public and our clients what we did and why. Helping you do that is what Standards is here for.
We hope to see you there!
Krista Jenkins
AAPOR Standards Chair