这个故事由Grist出品，与WIRED共同出品。 它是"扭曲"(Distorted)的一部分，是Grist系列在极端天气后探索错误信息的传播，并在CO2基金会的支持下出版。
本月早些时候，飓风伊萨亚斯作为三级飓风登陆海湾海岸后，应急管理专家劳拉·迈尔斯接到阿拉巴马州气象学家的电话。 Myers是阿拉巴马大学高级公共安全中心的主任，她经常与她所在地区的州和地方应急管理人员交谈。 气象学家告诉迈尔斯，当地一位应急管理人员发表了一张图片，显示伊萨亚斯可能用高风“喷气式飞机”来威胁阿拉巴马州海岸。
问题？紧急管理者使用人工智能聊天机生成图形
“该县的应急管理人员正试图让他的利益攸关方关注该县的风险，因此他请大赦国际帮助他，” “它做了他想要它做的事，但它在气象学上并不准确。”
大多数应急管理机构都不允许员工使用基因AI，但迈尔斯和其他大多数应急管理专家都认为，有上百家公司向应急管理人员推销AI工具，以完成洪水破坏评估和资源协调等任务，但公共传播是政府雇员和起步企业最感兴趣的领域之一。
原因吗？大多数应急管理人员并不善于发出警报，这不是因为他们是应急管理人员，他们倾向于用非专家可能无法理解的术语写作，不提供公众已经知道的信息，并包含不相关的细节，这些细节可能会转移人们对实际需要知道的事情的注意力。
“许多机构的资源和人数确实有限，”奥尔巴尼大学的灾害研究员称 Jeannette Sutton创建了一个全面的仪表板，为灾害警报提供模板。 “我认为，他们认为这是一个优先事项，但是，在意识到他们应该这样做之前，他们不优先。 他们只是想在不搞砸事情的情况下完成工作。”
应急管理人员是负责救灾和灾后恢复工作的政府官员。 在州和地方一级，正是这些官员及其机构，而不是联邦紧急事务管理局，构成了极端天气事件后的第一道防线。 因此，应急管理人员经常说，“信任是我们的货币，”他们在与公众的沟通中建立和失去这种信任。
随着气候灾害日益频繁和普遍，出现了许多人类通信有缺陷的事例，导致应急反应中断。 例如，2016年，加利福尼亚州相邻的县针对同一起野火发布了“自愿疏散”和“强制疏散”。 在2019年的澳大利亚，官员们使用“观察与法案”这一短语警告野火，政府后来的称造成了"重大混乱"，因为没有人知道它的含义。
在某些情况下，沟通不足可能意味着生死之分。 这发生在2017年"托马斯大火"后加利福尼亚州的蒙特西多。 火灾发生后数周，一场暴雨产生了致命的泥石流。 Santa Barbara县政府竭力向仍受大火创伤的居民传达危险，县政府向“自愿疏散区”的所有人发出野火警报，告诉居民“保持对不断变化的条件的警惕并准备立即离开”。 大部分居民应该立刻离开，但许多人认为疏散通知是可选的；泥石流造成23人死亡，160多人受伤。
称 Sutton), “他们失去了成为帮助人民安全的声音的机会。”
在理论上，警报是人工智能的完美使用案例，特别是对于过去几年来以戏剧性速度发展的基因AI系统而言。 FEMA为紧急情况管理者提供训练课程，一个大语言模型是不同的。 它可以在短短几分钟内读取数十万个警报，消化所有关于类似情景中起作用的研究，然后应用到几秒钟内编辑出人类警报。
“如果你一生中只送过一个[警报]，那是15年前的事了，你需要记住你的所有训练，而你在想，“嘿，我走过来了，电话响了，收音机响了，我需要帮助。” AI可能比你做得更好。 称 Rob Dale，一个前气象学家
戴尔已经用AI来起草一些幕后练习提示，因加姆县是密歇根州中部的一个小县，它不受飓风和火灾等重大天气事件的影响。 如果真的发生这样的事件，戴尔·称，他不会有任何问题允许一个聊天员起草他的事件通讯，但他仍然想控制发送警报。
一些专家认为，该技术已经足以部署。 EM1，一个为应急管理者生产AI软件的起步器，利用了Sutton的研究来训练一个其高管认为已经比几乎任何人类应急管理者都更好的模型。 公司在包括野火和海啸在内的多起场景中测试了这种内部模式。 在大多数基准测试测试中，它已经可以匹配或超过人的能力。
Sutton给Grist一个软件如何运作的例子：格里斯特向她提供了最近由一新墨西哥县发出的警报，该县试图就圣胡安河上的洪水向居民发出警告。
最初的警报是：“银行破碎。 取出水来。 (原始内容存档于2018-10-26). Evacuate Now. McGee Park对人和宠物开放，RV. 萨通要求EM1模式评价和修改警报。 在几秒钟内，它产生了她称的更好的警报，它检查了所有的灾难通信的主要箱子。
新的警报内容是：“圣胡安县：洪水正在靠近[地上升。 [撤离区]的人：现在离开，进入高地。 不要走或开车通过洪水。 麦基公园对人和宠物开放。 检查[官方更新来源]更新。” 该模型识别并解决了与原始文本的几个问题，该文本没有说明谁发出警报，指定发生洪水的具体地点，或澄清谁需要撤离，并就如何安全撤离提出建议。 它忽略了“违反”一词，也没有为居民寻找更多信息提供任何指导。
EM1与大约100个政府机构和校区签订合同，目前他们只用它来进行讲习班计划和内部准备工作。 即使如此，所有为这个故事采访过的专家，称都确信许多应急管理人员已经在使用聊天机来写信息，即使他们不承认这一点。 Sutton 称，参加她警戒训练的应急管理人员经常向她提供他们用ChatGPT或Gemini数秒后起草的高质量的警报。
称 Sarah Miller是佛罗里达州皮尔斯学院的应急管理教授，也是国家备灾训练中心的长期教官。 “他们使用自己的个人账户，或者使用免费账户。”
听起来是一个轻松的取舍：如果AI比人类更好，更快，而好警示和坏警示之间的区别可能是生与死，那么我们应该对每一个热浪和野火使用AI. 然而即使是那些在聊天室技术上畏缩的研究人员也说，开关可能会起反作用。 民众对AI持怀疑或不信任政府，例如，在纽约锡拉丘兹州，一个公共雇员工会控告地方政府利用AI服务接911非紧急电话。
称 Ginny Katz是HazAdapt的创始人，“大赦国际生成的内容不具有真实性。” 也存在着一种风险，即聊天机可以让应急管理人员发出更多的警报，更快，这可能会增加警告疲劳的风险。
与正在部署AI的其他领域一样，灾害专家说，目前软件只有使用软件的人才能使用。 一个老练的应急管理者也许可以通过一个聊天机器人起草一系列警报来加速他们与公众的沟通，但是一个经验不足的人急于建立紧急警报，并不知道如何评价ChatGPT或Claude等模型的输出。
虽然科技在循环中没有人类专家，至于AI生成内容的激增如何影响公众对政府的信任
“如果你有一个人给你发短信，而计算机给你发短信，你会认为哪一个更准确呢？” 称贾斯汀·斯奈尔(英語：称 Justin Snair)是普雷普尔的创始人，另一家公司向应急管理人员提供AI软件。
答案，He 称，取决于接收者。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
This story was produced by Grist and copublished with WIRED. It is part of Distorted, a Grist series exploring the spread of misinformation in the wake of extreme weather, and is published with support from the CO2 Foundation.
Earlier this month, after Hurricane Isaias made landfall on the Gulf Coast as a Category 3 hurricane, the emergency management expert Laura Myers got a call from a meteorologist in Alabama. Myers is director of the University of Alabama’s Center for Advanced Public Safety, and she talks to state and local emergency managers around her region all the time. The meteorologist told Myers that a local emergency manager had published a graphic showing where Isaias might threaten the Alabama coast with a “sting jet” of high winds.
The problem? The emergency manager had generated the graphic using an artificial intelligence chatbot, and it wasn’t accurate.
“That county emergency manager was trying to get his stakeholders to focus on the risk in that county, and so he had asked AI to help him,” she said. “It did what he wanted it to do, but it was not meteorologically accurate.”
Most emergency management agencies don’t permit employees to use generative AI, but Myers and most other emergency management experts agree that its usage is likely already pervasive at state and local disaster departments. There are hundreds of companies that are marketing AI tools to emergency managers for tasks like flood damage assessment and resource coordination, but public communication is one of the areas where government employees and startups have the most interest.
The reason? Most emergency managers aren’t very good at sending out alerts and quickly communicating about disasters with the public. That’s not because they’re emergency managers, but because they are human beings. They tend to write in jargon that non-experts might not understand, leave out information on the assumption that the public already knows it, and include irrelevant details that could distract people from what they actually do need to know.
“A lot of agencies are really limited in resources and the number of people that they have,” said Jeannette Sutton, a disaster researcher at the University of Albany who has created a comprehensive dashboard that provides templates for disaster alerts. “I think they recognize it as a priority, but they don't prioritize it until they realize they should have. They're just trying to get their job done without screwing something up.”
Emergency managers are the government officials who are in charge of disaster response and recovery. At the state and local level, It is these officials and their agencies, not the Federal Emergency Management Agency, that make up the first line of defense after an extreme weather event. As such, emergency managers often like to say that “trust is our currency,” and it’s in their communication with the public that they build and lose this trust.
As climate disasters become more frequent and widespread, there have been numerous examples of flawed human communication scrambling an emergency response. In 2016, for instance, adjacent counties in California issued a “voluntary evacuation” and a “mandatory evacuation” for the same wildfire. In Australia in 2019, officials warned about wildfires by using the phrase “Watch and Act,” which the government later said caused “significant confusion” because no one knew what it meant.
In some cases, inadequate communication can mean the difference between life and death. This happened in Montecito, California, after the 2017 Thomas Fire. A few weeks after the fire, a heavy rainstorm produced a deadly mudslide. The government of Santa Barbara County struggled to communicate the risk to residents still traumatized from the blaze and experiencing a phenomenon that Sutton calls “warning fatigue.” The county sent alerts to everyone in the “voluntary evacuation zone” for wildfires, telling residents to “stay alert to changing conditions and be prepared to leave immediately.” Most of those residents should have left immediately, but to many the evacuation notice was optional; others just didn’t want to evacuate for a second time in a month. The mudslide killed 23 people and injured more than 160 others.
Governments that put out bad warnings are apt to lose the public’s trust, said Sutton. “They're losing the opportunity to be that voice to help people to be safe.”
In theory, alerts are a perfect use case for artificial intelligence, and in particular for the generative AI systems that have developed at a dramatic pace over the past few years. FEMA provides training sessions for emergency managers to learn best practices for synthesizing and sending alerts, but many officials don’t remember all their training when an emergency arrives. A large language model is different. It can read hundreds of thousands of alerts in mere minutes, digest all the research on what works in similar scenarios, and then apply that to edit a human alert in seconds.
“If you’ve only sent one [alert] in your life, and that was 15 years ago, and you need to remember all your training, and you’re thinking ‘Hey, there's a chemical plume coming my way, the phone's ringing, the radio's blaring, I need help,’ the AI is probably gonna do a better job than you are,” said Rob Dale, a former meteorologist who now runs the emergency management department in Ingham County, Michigan.
Dale has already used AI to draft a few practice alerts behind the scenes, but he’s never had to send one out. Ingham County is a small county in central Michigan, and it’s immune from major weather events like hurricanes and fires. If such an event did happen, Dale said he would have no issue allowing a chatbot to draft his incident communication, but he’d still want control over sending the alert out.
Some experts believe the technology is already good enough to deploy. EM1, a startup that produces AI software for emergency managers, has used Sutton’s research to train a model that its executives say is already better than almost any human emergency manager. The company has tested this in-house model on a number of scenarios including wildfires and tsunamis. It can already match or exceed human capabilities on most benchmark tests.
Sutton gave Grist an example of how the software works: Grist provided her with a recent alert sent by a New Mexico county that was trying to warn residents about flooding on the San Juan River.
The original alert read, “Breeched banks. The water is rising. EVACUATE NOW. McGee Park open for people and pets, RV.” Sutton asked the EM1 model to evaluate and revise the alert. In a few seconds, it produced what she said is a far better alert, one that checks all the main boxes of disaster communication.
The new alert read: “San Juan County: Floodwater is rising near [location]. People in [evacuation area]: LEAVE NOW for higher ground. Do not walk or drive through floodwater. McGee Park is open for people and pets. Check [official update source] for updates.” The model identified and fixed several problems with the original text, which does not say who is sending the alert, name the specific location where flooding is happening, or clarify who needs to evacuate and give advice on how to do so safely. It misspells the word “breached,” and it doesn’t provide any guidance on where residents can seek further information.
EM1 has contracts with around a hundred government agencies and school districts, but none are using it to automate their extreme weather alerts just yet. For the moment, they’re only using it to workshop plans and internal preparations. Even so, all the experts interviewed for this story said they are certain that many emergency managers are already using chatbots to write messages, even if they won’t admit it. Sutton said that the emergency managers who take her alert training sessions often present her with high-quality alerts they have drafted in seconds using ChatGPT or Gemini.
“Official adoption is pretty low, but people are unofficially using it, absolutely,” said Sarah Miller, a professor of emergency management at Pierce College in Florida and a longtime instructor at the National Disaster Preparedness Training Center. “They're using their own personal accounts, or they're using free ones.”
It sounds like an easy trade-off: if AI is better and faster than humans, and the difference between a good alert and a bad alert can be life and death, we should be using AI for every heat wave and wildfire. Yet even researchers who are bullish on chatbot technology say that making the switch could backfire. Members of the public who are skeptical of AI or who don’t trust the government in the first place may spurn agencies that publish AI-generated graphics or social media posts. In Syracuse, New York, for instance, a union of public employees sued the local government for using an AI service to answer non-emergency 911 calls.
“We trust things that we perceive [are] authentic and care about us,” said Ginny Katz, the founder of HazAdapt, a consultancy that helps disaster officials figure out how to use technology. “AI-generated content does not feel authentic.” There’s also a risk that chatbots could allow emergency managers to send out more alerts, and faster, which could increase the risk of warning fatigue.
As is the case in other areas where AI is being deployed, disaster experts say that for the moment the software is only as capable as the person using it. A seasoned emergency manager might be able to speed up their communication with the public by having a chatbot draft a series of alerts, but an inexperienced one rushing to create an urgent alert won’t know how to evaluate the output of a model like ChatGPT or Claude.
Even though the technology is a ways off from being able to perform without a human expert in the loop, that hasn’t stopped many understaffed emergency management departments from using it. As for how the proliferation of AI-generated content is affecting public trust in government, experts say it’s too early to tell.
“If you had a human sending you a text message versus a computer sending you a text message, which one would you believe is being more accurate?” said Justin Snair, the founder of Preppr, another company that offers AI software to emergency managers.
The answer, he said, depends on the person receiving it.
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