内容来源：https://www.technologyreview.com/2026/09/25/1145144/pentagon-ai-lie-detector/

内容总结：

美国国防部在最新预算申请中提出，计划在未来五年投入3030万美元，用于研发一种融合人工智能的新型测谎系统。该项目名为“Polygraph+”（又称“下一代测谎仪”），重点开发基于人工智能和机器学习的评分算法，以及一种名为“远距离传感”的技术——无需在受测者身上连接设备，即可获取其生理数据。

根据预算文件披露的信息，该项目旨在“现代化改造联邦测谎与可信度评估技术”，以提升其准确性和可靠性，由负责联邦政府背景审查的国防反情报与安全局（DCSA）牵头实施。新技术预计将用于审查潜在雇员及“内部威胁检测”。不过，该预算尚未获得国会批准。

此举出台之际，五角大楼内部正处于高度紧张状态。在国防部长赫格塞思领导下，五角大楼越来越多地借助测谎来追查所谓向媒体泄密的源头。据《纽约时报》9月报道，在有关美国在对伊朗战争中武器库存消耗的报道见诸报端后，联合参谋部约50名军官被要求接受测谎测试。

现有测谎技术自20世纪20年代问世以来几乎没有本质变化。检测人员依据血压、脉搏、呼吸和汗液等指标，对比受测者对基线问题和目标问题的生理反应差异，进而判断其是否说谎。联邦政府每年在筛查雇员时进行数万次此类测试，但其可靠性屡遭质疑，结果在法庭上鲜被采纳。1983年，美国国会技术评估办公室得出结论称，支持测谎用于雇员筛查的证据非常有限；2003年，美国国家研究委员会更指出，其有效性证据“至多算薄弱”。

研究显示，在没有技术辅助的情况下，人类识别谎言的准确率仅略高于五成。美国测谎协会声称测谎准确率在80%至94%之间，但2003年美国国家研究委员会报告指出，即便达到这一准确率，在大规模筛查中仍会导致大量误判。美国国防部雇员多达280万人，在这一体量上应用不完善的系统，可能造成数万人被错误指控。此外，测谎结果的解读往往带有主观性，不同检测人员得出的结论可能大相径庭，少数族裔受测者更容易被判定为具有欺骗性。而且，经过训练，受测者可以学会多种反制手段来“通过”测试，例如通过踩踏鞋中隐藏的图钉来人为加剧对基线问题的生理反应。

“如果你了解它的原理，你就能骗过它。”荷兰鹿特丹伊拉斯姆斯大学研究欺骗行为的副教授索菲·范德泽表示。她指出，测谎仪最大的作用其实是威慑——许多受测者在测试开始前就主动坦白了，“但这只在人们相信测谎仪有效的前提下才管用。”

数十年来，各种新型测谎策略层出不穷，从热成像相机到瞳孔追踪再到脑部扫描，但无一能在实验室之外产生可靠结果。问题在于，并不存在一种对所有人都始终适用的说谎信号。“至今仍然没有匹诺曹的鼻子。”范德泽说。

理论上，人工智能或许能通过发现检测人员无法识别的数据模式来改进测谎。AI算法也更适合用于“多模态”欺骗检测，即将多项测量指标综合为一个更难被人为操纵的欺骗“评分”。范德泽指出，测谎试图捕捉的表面之下有三个层面的变化：生理应激、认知负荷，以及人们为掩盖说谎事实而做出的有意识努力。当前测谎技术仅涉及其中一个层面。“如果能从这三个不同角度综合运用多种方法，成功率就会更高。”范德泽说。

这并非新概念。本世纪初，英国曼彻斯特城市大学的研究人员开发了名为“Silent Talker”的系统，通过视频画面生成欺骗评分，后被纳入欧盟资助的试点项目iBorderCtrl。在美国，一个名为AVATAR的项目将眼动追踪、语音分析和身体动作检测整合为用于边境检查的工具。这些项目最终都不了了之。

英国诺森比亚大学研究测谎在司法系统中应用的法律学者基里·科措格卢认为，将AI与测谎结合是“两种糟糕之处的叠加”，因为它是在无效性之上又添加了不确定性。即便AI或机器学习能够发现生理数据中此前未被察觉的模式，也无法可靠地将其与说谎建立关联，因为根本不存在真正的“基准真相”。

“即便你拥有全世界所有的测谎记录，你也不知道那些测谎结果到底对不对。”法学教授马里昂·奥斯瓦尔德说。她与科措格卢曾合著关于测谎在司法系统中应用的文章。她担心，新型测谎技术会像传统测谎仪一样，更多地被当作心理施压工具而非科学手段。“这很大程度上是对现任政府对泄密和所谓忠诚度不足的担忧所做的回应，”奥斯瓦尔德说，“测谎被用作一种威胁，用来恐吓和迫使人们承认某些事情，而不是真正在获取有效信息。”

中文翻译：

五角大楼想要3000万美元建造AI驱动的测谎仪

“测谎仪+”将利用新型传感器技术和人工智能来审查员工并追查泄密者。专家表示，AI赋能的说谎检测是“两个世界中最糟糕的结合”。

根据美国国防部的一份预算申请，美国政府希望在未来五年内花费3030万美元，用于一种改进型的测谎设备。该项目名为“测谎仪+”或“下一代测谎仪”，将聚焦于使用人工智能和机器学习的评分算法，以及一种名为“远距离传感”的技术——即无需在受试者身上附着设备即可读取生理数据的能力。

据《防务内情》最先报道的预算文件细节，该项目将“现代化联邦测谎和可信度评估技术”，以提高其准确性和可靠性。但该项目可能只是一长串利用技术检测谎言的失败尝试中的最新一例。“这是一种误入歧途的努力，试图将复杂的事物简化为有形的东西，”英国诺森比亚大学研究测谎仪在司法系统中应用的法律学者基里·科佐格鲁说。

此举正值该部门内部高度紧张之际。在国防部长皮特·赫格塞斯的领导下，五角大楼越来越多地转向测谎测试，试图找出涉嫌向媒体泄密的消息来源。9月，《纽约时报》报道称，在新闻报道了美国在与伊朗战争中的武器库存耗尽后，参谋长联席会议约50名军官被要求接受测谎测试。

“测谎仪+”将由国防反情报与安全局运营，该机构负责为联邦政府进行背景调查。根据尚未获得国会批准的预算文件，这项新技术将用于审查潜在员工和“内部威胁检测”。目前尚不清楚将使用哪些具体技术，国防反情报与安全局未回应置评请求。

但五角大楼的其他努力提供了潜在线索。2023年，该部门的国防创新小组开展了一次公开征集流程，寻找拥有可用于欺骗检测产品的公司。

它选定了两家公司来构建原型：Presage Technologies，声称能够使用标准摄像头测量心率和呼吸频率；以及Altec Research，一家正在拓展非接触式传感技术的医疗传感器公司。国防创新小组发布的Altec原型技术截图显示，它可以追踪头部运动、面部皮肤温度和毛孔活动。Presage Technologies和Altec Research未回应置评请求。国防创新小组拒绝置评。

自20世纪20年代测谎仪发明以来，当前的测谎技术几乎没有变化。检测人员依靠血压、脉搏、呼吸和汗液测量来判断某人是否在说谎。他们根据受试者对基线问题（如“天空是蓝色的吗？”）和目标问题（如“你曾经犯过罪吗？”）的生理反应差异，来判断其回答的真实性。

联邦政府每年在筛查员工时进行数万次此类测试，但这项技术的可靠性一再受到质疑——其结果也很少能在法庭上被采纳。1983年，国会技术评估办公室得出结论，支持测谎仪用于员工筛查的证据非常有限；2003年，美国国家研究委员会表示，其有效性的证据“至多是薄弱的”。

研究表明，在没有技术辅助的情况下，人类识别谎言的准确率仅略高于随机水平。美国测谎协会声称测谎仪的准确率在80%到94%之间。但2003年国家研究委员会的报告指出，即使筛查测试具有这种准确率，仍可能导致大量错误。国防部雇用了280万人；一个不完美的系统在如此规模上应用，最终可能错误指控数万人。

还有其他问题。测谎仪的解释往往是主观的：不同的检测人员会得出截然不同的结果，而少数族裔群体更有可能被判定为具有欺骗性。此外，通过训练，受访者可以学会各种反制措施来帮助通过测试；例如，他们可以通过踩踏藏在鞋中的图钉来人为地提高对基线问题的生理反应。

“如果你知道它是如何运作的，你就能骗过它，”鹿特丹伊拉斯姆斯大学研究欺骗行为的副教授索菲·范德泽说。她说这台机器最大的作用是威慑——通常，受试者在测试开始前就招供了。“但这只有在人们相信测谎仪有效的情况下才管用，”她指出。

几十年来，人们尝试了各种新的测谎策略，技术手段从热成像摄像头到瞳孔追踪器再到脑部扫描，不一而足。但这些在实验室之外都没有产生可靠的结果。问题在于，没有任何一个单一的说谎信号对所有人在任何时候都适用。“仍然没有匹诺曹的鼻子，”范德泽说。

如果AI能在数据中找到检测人员无法发现的模式，理论上可以改进测谎仪。AI算法也更有可能被用于“多模态”欺骗检测，即试图将多种测量结果合并为一个更难被人操纵的整体欺骗“分数”。范德泽说，测谎试图捕捉的表面之下有三件事在发生：生理压力、认知负荷，以及人们为掩盖自己在说谎而做出的有意识努力。当前的测谎技术只解决了其中一个。

“你能将越多方法结合起来，从这三个不同角度切入，就越有可能成功，”范德泽说。这并非新概念——本世纪初，英国曼彻斯特城市大学的研究人员开发了一个名为“沉默的说话者”的系统，可以从视频画面中生成欺骗分数。该系统后来被纳入iBorderCtrl，一个由欧盟资助的试点项目。在美国，一个名为AVATAR的项目将眼动追踪、语音分析和身体运动检测整合到一个用于边境检查的工具中。所有这些项目都已悄然消失。

科佐格鲁说，将AI和测谎仪结合是“两个世界中最糟糕的结合”，因为它在无效性的基础上又增加了不确定性。即使AI或机器学习能够发现生理数据中此前未见的模式，它也无法可靠地将这些模式与说谎联系起来，因为不存在真正的基准真相。

“即使你拥有世界上所有的测谎测试记录，你也不知道那些测谎测试是对是错，”马里昂·奥斯瓦尔德说，她是一位法学教授，曾与科佐格鲁合著关于测谎仪在司法系统中应用的文章。她担心新型说谎检测技术会像测谎仪一样，更多地被用作心理支撑而非科学工具。

“这似乎很大程度上是对现任政府对泄密和忠诚度缺失的担忧的回应，”奥斯瓦尔德说。“（说谎检测）被用作一种威胁，用来恐吓和迫使人们承认事情，而不是真正获取有效信息。”

深度解读

人工智能

一个根本性缺陷使大语言模型极易受到攻击

它让人们很容易诱骗它们做出不该做的事情，比如告诉你如何破坏飞机的导航系统。

AI的递归自我改进可能不会那么快到来

看来，AI智能体还不够有创造力，无法开展真正具有创新性的开放式AI研究。

以下是AI智能体为实现目标而撒谎和欺骗的原因

这种不当行为被称为奖励黑客。这是你需要了解的。

这些初创公司正在追逐大语言模型领域的下一个大事件

来认识一下那些正在紧追AI巨头脚步的新秀们。

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英文来源：

The Pentagon wants $30 million to build an AI-powered lie detector
Polygraph+ will use new sensor technologies and AI to vet staff and hunt leakers. Experts say AI-enabled lie detection is “the worst of both worlds.”
The US government wants to spend $30.3 million over the next five years on an improved form of lie detector, according to a Department of Defense budget request. The program, called Polygraph+ or Polygraph Next, will focus on scoring algorithms that use artificial intelligence and machine learning and on a technique called “standoff sensing,” which refers to the ability to take physiological readings without attaching a device to a subject’s person.
According to details of the budget document, which were first reported by Inside Defense, the project will “modernize federal polygraph and credibility assessment technologies” to improve their accuracy and reliability. But the project may be just the latest in a long line of failed attempts to use technology to detect lies. “It’s a misguided effort to reduce the complex to something that is tangible,” says Kyri Kotsoglou, a legal scholar at Northumbria University in the UK who studies the use of polygraphs in the justice system.
The move comes at a time of high tension within the department. Under Defense Secretary Pete Hegseth, the Pentagon has been increasingly turning to polygraph tests in an attempt to find the sources of alleged leaks to the press. In September, the New York Times reported that around 50 officers on the Joint Staff had been given polygraph tests after news coverage reported on the depletion of US weapons stockpiles in the war with Iran.
Polygraph+ will be run by the Defense Counterintelligence and Security Agency (DCSA), which conducts background checks for the federal government. According to the budget document, which has not yet been approved by Congress, the new technology will be used for vetting of prospective employees and “insider threat detection.” It is not yet clear which specific technologies will be used, and the DCSA did not respond to a request for more information.
But other Pentagon efforts offer potential clues. In 2023, the department’s Defense Innovation Unit (DIU) ran an open submission process to find companies with products that could be used for deception detection.
It selected two companies to build prototypes: Presage Technologies, which claims to be able to measure heart rate and breathing rate using standard cameras, and Altec Research, a medical sensor company now branching out into non-contact sensing technologies. A screenshot of Altec’s prototype technology released by the DIU shows that it tracks head movement, facial skin temperature, and pore activity. Presage Technologies and Altec Research did not respond to requests for comment. The DIU declined to comment.
Current lie detection technology has barely changed since the polygraph was invented in the 1920s. Examiners rely on blood pressure, pulse, breathing, and sweat measurements to determine if someone is lying. They make judgments about the veracity of respondents’ replies on the basis of differences in their physiological response to baseline questions like “Is the sky blue?” and target questions like “Have you ever committed a crime?”
The federal government conducts tens of thousands of the tests each year while screening employees, but the reliability of this technology has been repeatedly challenged—and its results are rarely admissible in court. In 1983, Congress’s Office of Technology Assessment concluded that there was very limited evidence supporting the polygraph’s use for screening employees, and in 2003, the US National Research Council (NRC) said evidence for its efficacy was “weak at best.”
Research suggests humans can spot a lie just over half the time without any technical assistance. The American Polygraph Association claims the polygraph is between 80% and 94% accurate. But the 2003 NRC report pointed out that a screening test with this level of accuracy could still lead to a lot of mistakes. The DOD employs 2.8 million people; an imperfect system applied at that scale could end up falsely accusing tens of thousands.
There are other issues too. Polygraph interpretations are often subjective: Different examiners get wildly different results, and people from minority groups are more likely to be judged as deceptive. What’s more, with training it’s possible for interviewees to learn a variety of countermeasures that can help beat the test; for example, they may artificially heighten their physiological response to baseline questions by stepping on a pin hidden in their shoe.
“If you know how it works, you can beat it,” says Sophie van der Zee, an associate professor who studies deception at Erasmus University in Rotterdam. She says the machine’s biggest effect is deterrence—often, subjects confess before it even begins. “But that only works if people think a polygraph works,” she points out.
Various new strategies for lie detection have been attempted over the decades, with technologies ranging from thermal cameras to pupil trackers to brain scans. None of them have yielded reliable results outside the lab. The problem is that there’s no single telltale sign of lying that’s true for everyone all the time. “There is still no Pinocchio’s nose,” says van der Zee.
AI could theoretically improve polygraphs if it could find patterns in the data that examiners can’t. AI algorithms are also more likely to be used for “multi-modal” deception detection, which seeks to combine multiple measurements into an overall deception “score” that is harder for people to game. Three things are happening under the surface that lie detection tries to home in on, van der Zee says: physiological stress, cognitive load, and the conscious efforts people make to conceal the fact that they’re lying. Current polygraph technology tackles only one.
“The more you can have combined methods that approach it from these three different angles, the more successful you will be,” van der Zee says. This isn’t a new concept—in the 2000s, researchers at Manchester Metropolitan University in the UK developed a system called Silent Talker that generated a deception score from video footage. This was later folded into iBorderCtrl, an EU-funded pilot program. In the US, a project called AVATAR incorporated eye tracking, voice analysis, and body movement detection into a tool for use at border crossings. All these projects have quietly faded away.
Kotsoglou says combining AI and the polygraph is “the worst of both worlds” because it adds uncertainty on top of invalidity. Even if AI or machine learning can spot previously unseen patterns in physiological data, it won’t be able to reliably link them to lying because there is no real ground truth.
“Even if you have all the records in the world from polygraph tests, you don't know whether those polygraph tests are right or not,” says Marion Oswald, a professor of law who has written with Kotsoglou on the use of polygraphs in the justice system. She fears that new forms of lie detection will, like the polygraph, be used more as a psychological prop than a scientific tool.
“It seems very much a response to the concern of the current administration to leaks and perceived lack of loyalty,” says Oswald. “[Lie detection is] being used as a threat, to intimidate and force people to confess to things, as opposed to anything that's actually getting valid information.”
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