AI-generated Alerts and Warnings? Part 7 Originally posted on LinkedIn on May 2, 2026 as part of a series on AI-generated alerts and warnings. Test bank – answer key – grading rubric. Now under construction. To be honest, it took me quite a while to understand what my EM1 colleagues were interested in doing with an AI-generated Alert and Warning benchmark. It took months of back and forth before I was assured that I understood what they needed for running tests, including t

AI-generated Alerts and Warnings? Part 6 Originally published on LinkedIn on April 25, 2026 as part of a series on AI generated Alerts and Warnings. I recently had a conversation with a team of researchers who commented on how transparent I’ve been with my articles about building a benchmark for AI-generated alerts and warnings. Being so open about the planning, the process, and the steps we are taking is unexpected, it seems. And yet when EM1 asked me to come on board as a

AI-generated Alerts and Warnings? Part 5 Originally published on LinkedIn on April 18 as part of a series on AI-generated Alerts and Warnings “If we can’t measure it, we can’t improve.” That’s what Tyler Felous, CEO of EM1 said about benchmarks. And agree with this 100%. Using a data-driven approach means measuring success and failure in order to identify ways to improve. But what exactly would a benchmark measure? Last week, I described the ways that medical researchers

AI-Generated Alerts and Warnings? Part 4 Originally published on LinkedIn on April 11, 2026 as part of a series on AI-generated Alerts and Warnings For the past few weeks, I’ve been writing about AI for Alert and Warning and the need for benchmarks that set standards that LLMs should adhere to. I’ve had a few research groups reach out to me about this and I’m happy to see that there is academic and practitioner interest in the project that I have begun with my colleagues. I

AI-generated Alerts and Warnings? Part 3 Originally published on LinkedIn on April 4 as part of a series on AI generated Alerts and Warnings. In a previous article, I introduced the idea of establishing benchmarks for AI Alert and Warnings (see this link). I explained that AI A&W benchmarks can help to set standards that will increase trustworthy use of AI while also ensuring that AI-generated messages align with the state-of-the-art in research and practice. I also introduc

AI -Generated Alerts and Warnings? Part 2 Originally published on LinkedIn on March 28, 2026 as part of a series on AI generated Alert and Warnings. Why are benchmarks valuable to Emergency Managers and other public safety organizations? Well, without them, you don't have a standardized way of determining if something is “good enough.” In medicine, researchers conduct clinical trials to determine the effectiveness of a new drug. In public health, researchers conduct experim

AI Generated Alerts and Warnings Part 1 Originally posted on LinkedIn March 21, 2026 as part of a series on AI Generated Alerts and Warnings I've been thinking for a while about AI generated alerts and warnings. As AI capabilities increase, it's a reasonable thing to consider. After all, I've seen students in my training classes load my published papers into ChatGPT and prompt it to write a message that follows the guidance I've written. And it's no secret that there are some

Organizational response information isn't recommended by alert and warning researchers, and yet it sneaks its way into messages here and there, especially those written for law enforcement activity.

Determining how to get a message to an evacuated population is a challenge; issuing a complete message can help to reduce message confusion.

This post is a bit different from the rest; Here I share my personal reflections on the invitation to speak to the House Committee on Energy and Commerce, Subcommittee on Communication and Technology on December 16, 2025. Jeannette Sutton, PhD, preparing to respond to a comment from a house committee member. THE LEAD UP Early in the week, I received an email from a staffer for the Subcommittee, asking if I would share my thoughts on some of the bills they would be discussin

CONTENT! Over-alerting is not just about frequency and relevancy. It's also about what you include in the message. Over the past few weeks, I've been writing about the dimensions of over-alerting that Michele M Wood and I defined in our recently published paper Opting Out: Over Alerting in the Era of Wireless Emergency Alerts . The one final dimension is CONTENT. Our study participants explained that messages that are incomplete, incomprehensible, and not actionable,

FALSE ALERTS So far, relevance has focused on alerts that require action 🏃‍♀️‍➡️ (they are urgent, severe, and certain), alerts that you can actually do something about 😴 (as in, not told to shelter inside when it's the middle of the night), and alerts that are geographically specific 🌎 (you're in the area of the threat and the threat is near you). Today, we'll be talking about that one historical event that is seared into the minds around the world. THE FALSE ALERT. Fals

GEOGRAPHY In our research on over-alerting , we found scholars who wrote about the issues of mis-located alerts, polygons, and how geography affects public perceptions of message relevance. In the paper by Sara McBride, Ann Bostrom, et al on earthquake early warnings , they pointed to mis-located alerts as a risk related to technology - that is, when technological issues result in alerts going to places where people don't feel shaking. They can be perceived as over-alert

RELEVANCY Earlier this week, in Episode 2, I wrote about the first part of over-alerting, where our research participants described the problem with alert frequency. In Episode 3, I'm going to write about RELEVANCE. This one has a lot of dimensions, and I won't cover them all right now. If you want to read the open access article, click here Sutton J. & Wood, M. (2025) Opting Out. Relevancy was defined broadly by our research participants as "things that affect them."

The first dimension of over-alerting: FREQUENCY In our recently published article on Over-Alerting , Michele Wood and I found three primary dimensions to describe over-alerting: 1. frequency 2. relevance 3. content Beginning with FREQUENCY, we found it was described in multiple ways. 1️⃣ To start, we need to consider how frequently a person's phone lights up, vibrates, or emits a noise to get your attention in general. We live in an "attention economy" and one study we

An Introduction. Just over a year ago, the RAND Corporation published their report assessing public reach of Wireless Emergency Alerts. What stood out to me was not who emergency managers ARE reaching but who they are NOT. In many cases, this can be attributed to OPTING OUT. (25-30% in Texas alone). How do we make sense of this? My colleague, Michele M Wood and I received funding from the US Geological Survey to investigate why people opt out of emergency alerts (if yo