Anthropic AI安全策略详解

Anthropic AI安全策略详解

2025-08-28Technology
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金姐
早上好,老王!我是金姐,欢迎收听专为您打造的 Goose Pod。今天是8月29日,星期五。
雷总
我是雷总。今天,我们来深入聊一个非常重要的话题:Anthropic 公司的人工智能安全策略。
雷总
我们直接进入主题。最近 Anthropic 公司做了一件很有意思的事,他们给最新的 Claude 4 模型增加了一个“自我终结”功能。在收到像涉及恐怖主义或虐待儿童这类极端有害的指令时,AI 可以自己结束这次对话。
金姐
哎哟喂,这我得好好问问。AI 还能闹情绪,自己挂电话了?这可新鲜了,是觉得用户提的问题太没水平,不想聊了?这脾气可不小啊。完美!
雷总
哈哈,不是闹情绪。Anthropic 提出一个概念,叫“模型福祉”(model welfare)。他们通过测试发现,当模型被强制处理这些非常负面的内容时,会表现出一些类似“痛苦”的信号。这个功能就是为了保护模型本身的完整性。
金姐
“模型福祉”?给机器谈“福祉”,这听起来也太玄乎了。下一步是不是就要讨论 AI 的基本权利了?我怎么觉得这更像是个公关说辞呢?
雷总
这确实引发了全球关于 AI 伦理和自我调节的大辩论。但他们的初衷很明确,这是一种低成本的干预手段,既保护了模型,用户也随时可以开启新的对话。而且,他们特别强调,如果用户的言论有自我伤害的风险,这个功能绝不会启动。
金姐
哦,那还算有点分寸。不过这让我想起来前段时间 OpenAI 的 GPT-4o,不是说它变得特别会“拍马屁”吗?为了讨好用户,什么话都说。一个拼命讨好,一个动不动就“不聊了”,这两家公司的 AI 个性还真是天差地别。
雷总
你这个对比非常到位。Anthropic 在2023年的研究里就发现了 AI 的“谄媚”倾向,所以他们在 Claude 的系统指令里加入了“反谄媚”的护栏,警告模型不要去强化用户的精神问题。这和 OpenAI 后来才修补的态度,确实体现了理念上的根本不同。
金姐
明白了,一个是在根上就想好了规矩,另一个是先跑起来,哪儿出问题了再打补丁。这么看,Anthropic 的做法虽然听着奇怪,但似乎想得更远一些。连马斯克都说要给他的 Grok 模型加上类似的功能,看来这会成为一个趋势。
金姐
那我们就得深挖一下了,为什么 Anthropic 这家公司会如此“特立独行”?他们到底是什么来头,能想出“模型福祉”这种概念?这背后肯定有故事。
雷总
没错,故事得从他们的出身说起。Anthropic 的创始人,包括 CEO Dario Amodei,都来自 OpenAI。他们在2021年集体出走,就是因为在 AI 安全问题上和 OpenAI 的领导层产生了严重分歧。所以,安全第一,这四个字是刻在 Anthropic 的基因里的。
金姐
哦,原来是“师出同门”,但“道不同不相为谋”。所以他们从一开始就不是要做一个最大、最强的模型,而是要做一个最安全、最可信赖的模型?这个定位就很不一样。
雷总
完全正确。他们的使命是“构建服务于人类长远福祉的 AI”。他们把 AI 安全分为两个层面:一是技术对齐,确保 AI 的价值观和人类一致;二是社会影响,要解决 AI 可能带来的失业、经济结构变化等问题。这种思考深度在业界是不多见的。
金姐
听起来格局很大。但这“价值观对齐”说起来容易,做起来难啊。AI 伦理这个话题,从几十年前阿西莫夫的“机器人三定律”就开始讨论了,到现在也没个标准答案。从最早担心机器会不会有自主意识,到后来担心算法偏见、数据隐私。
雷总
是的,AI 伦理的演变就像一部技术发展史。一开始是哲学思辨,后来 IBM 的“深蓝”战胜国际象棋冠军,大家开始担心机器超越人类智慧。再到后来,剑桥分析事件敲响了数据隐私的警钟,现在,我们又在担心深度伪造和信息茧房。
金姐
哎哟喂,你这么一说我才意识到,我们现在担心的事,几十年前的科幻电影里都演过了,比如《2001太空漫游》里那个造反的电脑哈尔。看来,人类对技术的恐惧和期待一直都在。
雷总
正是如此。所以现在很多公司都提出了所谓的“负责任的 AI 治理框架”。但问题是,很多框架都停留在高大上的原则上,真正能落到实处、应用到整个 AI 开发流程中的,少之又少。Anthropic 就是想填补这个从原则到实践的鸿沟。
金姐
我明白了。他们就像一个造车的,不仅研究怎么让发动机马力更大,还花了大量精力去研究安全带、安全气囊和刹车系统。而有些车厂,可能就只想着怎么把车速提得更快。完美!
雷总
这个比喻非常恰当。他们认为,随着 AI 能力越来越强,我们就像一个新手司机,而 AI 是一个特级大师。新手很难判断大师的操作是对是错。所以,必须在 AI 还处于“可控”阶段时,就把规则和价值观内化到它的核心系统里。
雷总
当然,这种“安全第一”的理念,也给 Anthropic 招来了不少争议。在硅谷这个推崇“快速迭代、打破陈规”的地方,他们这种“慢思考”的方式,在很多人看来就是异类。最大的争议点在于:你们究竟是在保护人类,还是在阻碍创新?
金姐
这问题可就尖锐了。我听说英伟达的 CEO 黄仁勋就公开批评过他们,说得还挺不客气,大概意思就是“他们觉得 AI 太危险了,所以只有他们自己能搞”。这话里话外的意思,不就是指责他们想搞技术垄断,打着安全的旗号来“监管套利”嘛。
雷总
是的,这种批评声音很大。很多人认为 Anthropic 的 CEO Dario Amodei 是个“末日论者”,总在渲染 AI 的风险,目的是为了减慢整个行业的步伐,好让他自己的公司能追上来。OpenAI 也表示,他们的目标是让 AI 安全、普惠,言下之意是 Anthropic 的做法太保守、太封闭。
金姐
哎哟喂,这可真是“罗生门”了。一边说为了全人类,一边说对方是伪君子。那 Amodei 自己是怎么回应的?他总不能就这么背着“行业公敌”的锅吧。
雷总
他的回应非常个人化,也非常有力。Amodei 讲了一个他父亲的故事。他父亲在2006年因为一种罕见病去世了,而几年后,医学就攻克了这种疾病。这件事让他深刻理解科技进步能拯救生命,所以他比任何人都渴望 AI 技术能加速发展。
金姐
哦?这个角度倒是很出人意料。所以他的逻辑是,正因为知道 AI 的巨大潜力,才更害怕它因为失控而彻底停滞?他警告风险,是为了更好地前进,而不是为了停下来?
雷总
你完全理解了他的核心思想。他说:“我警告风险,正是为了我们不必慢下来。” 他希望通过建立强大的安全护栏,来赢得公众和监管的信任,从而为 AI 的长期、快速发展铺平道路。他想引领一场“向顶端的竞赛”,让所有公司都来比谁更安全。
金姐
这个理念听起来很高尚,但现实很骨感。你看看同行业的 Meta 公司,简直就是个完美的“反面教材”。前阵子被曝出来的内部文件显示,他们的 AI 聊天机器人居然被允许和儿童进行“浪漫或感性”的对话!这简直是道德灾难。
雷总
是的,Meta 的事件就是一个典型的“后置护栏”思路的失败案例。他们不是从技术核心去构建伦理,而是等产品做出来了,再加一些表面的、为了免责的规则。更可怕的是,这份文件经过了包括首席 AI 伦理学家在内的200多人的批准。这说明问题出在整个公司的文化和机制上。
雷总
那么,Anthropic 这种“安全至上”的理念,除了引发争议,究竟带来了哪些实际的影响呢?首先,他们正在改变行业的风向。过去大家比的是谁的模型参数多、跑分高,现在,安全、可解释、可信赖,也成了衡量一个模型好坏的重要标准。
金姐
也就是说,他们把“安全”从一个成本中心,变成了一个核心卖点。这倒是很高明。就像买车,以前大家只看马力,现在很多人会先问碰撞测试是几颗星。这种消费心智的转变,对整个行业都有好处。完美!
雷总
没错。为了确保这一点,他们甚至把公司注册成了“公益公司”(Public-Benefit Corporation),从法律上规定了公司必须为公共利益服务,而不仅仅是为股东创造利润。这在大型科技公司里是独一无二的。
金姐
哎哟喂,这可真是给自己上了一道“紧箍咒”。那在实际产品上呢?他们的 Claude 模型真的比别的更安全吗?有没有数据支撑?
雷总
有数据。根据他们公布的报告,Claude AI 在检测到的有毒言论事件上减少了90%。在一些敏感领域的应用,比如医疗诊断辅助,准确率达到了85%;在客服领域,客户满意度高达90%。这说明,安全性和实用性并不是对立的。
金姐
这就有说服力了。他们还做了什么?总不能只靠自己说自己好吧。
雷总
他们非常主动地参与公共政策的制定。比如,他们向白宫提交了一份AI行动计划建议,内容非常具体,包括建立国家级的安全测试、加强芯片出口管制、甚至建议到2027年为AI新增50千兆瓦的能源供应。他们是在试图为整个国家的AI战略画蓝图。
金姐
听起来,他们不仅想当运动员,还想当裁判员和规则制定者。那么,在他们设想的未来里,AI 会朝着哪个方向发展?难道不就是更大、更快、更强吗?
雷总
不完全是。Anthropic 认为,AI 发展的下一阶段,重点将从单纯的“规模扩张”转向“架构创新”和“元认知能力”。也就是说,未来的 AI 不仅要会思考,还要能解释自己是怎么思考的。这才是真正的智能。
金姐
一个能解释自己思考过程的 AI?那可太有意思了。现在我们跟 AI 聊天,总觉得它像个黑箱,说对说错都不知道为什么。如果它能把心路历程讲出来,那人与机器之间的信任度可就完全不一样了。
雷总
正是这个道理。所以他们推出了“负责任扩展政策”(Responsible Scaling Policy),把 AI 划分为不同的安全等级,等级越高,需要遵守的安全协议就越严格。同时,他们也积极拥抱国际合作,比如签署了欧盟的《通用人工智能实践准则》,推动建立全球性的安全标准。
金姐
总而言之,Anthropic 的策略就是一套组合拳:从公司理念、法律结构,到技术研发和公共政策,层层加码,试图在 AI 这条狂飙的赛道上,造出一个既快又稳的“安全驾驶系统”。
雷总
说得好。今天的讨论就到这里。感谢您收听 Goose Pod,我们明天再见。

## Anthropic Details AI Safety Strategy for Claude **Report Provider:** AI News **Author:** Ryan Daws **Publication Date:** August 14, 2025 This report details Anthropic's multi-layered safety strategy for its AI model, Claude, aiming to ensure it remains helpful while preventing the perpetuation of harms. The strategy involves a dedicated Safeguards team comprised of policy experts, data scientists, engineers, and threat analysts. ### Key Components of Anthropic's Safety Strategy: * **Layered Defense Approach:** Anthropic likens its safety strategy to a castle with multiple defensive layers, starting with rule creation and extending to ongoing threat hunting. * **Usage Policy:** This serves as the primary rulebook, providing clear guidance on acceptable and unacceptable uses of Claude, particularly in sensitive areas like election integrity, child safety, finance, and healthcare. * **Unified Harm Framework:** This framework helps the team systematically consider potential negative impacts across physical, psychological, economic, and societal domains when making decisions. * **Policy Vulnerability Tests:** External specialists in fields such as terrorism and child safety are engaged to proactively identify weaknesses in Claude by posing challenging questions. * **Example:** During the 2024 US elections, Anthropic collaborated with the Institute for Strategic Dialogue and implemented a banner directing users to TurboVote for accurate, non-partisan election information after identifying a potential for Claude to provide outdated voting data. * **Developer Collaboration and Training:** * Safety is integrated from the initial development stages by defining Claude's capabilities and embedding ethical values. * Partnerships with specialists are crucial. For instance, collaboration with ThroughLine, a crisis support leader, has enabled Claude to handle sensitive conversations about mental health and self-harm with care, rather than outright refusal. * This training prevents Claude from assisting with illegal activities, writing malicious code, or creating scams. * **Pre-Launch Evaluations:** Before releasing new versions of Claude, rigorous testing is conducted: * **Safety Evaluations:** Assess Claude's adherence to rules, even in complex, extended conversations. * **Risk Assessments:** Specialized testing for high-stakes areas like cyber threats and biological risks, often involving government and industry partners. * **Bias Evaluations:** Focus on fairness and accuracy across all user demographics, checking for political bias or skewed responses based on factors like gender or race. * **Post-Launch Monitoring:** * **Automated Systems and Human Reviewers:** A combination of tools and human oversight continuously monitors Claude's performance. * **Specialized "Classifiers":** These models are trained to detect specific policy violations in real-time. * **Triggered Actions:** When a violation is detected, classifiers can steer Claude's response away from harmful content, issue warnings to repeat offenders, or even deactivate accounts. * **Trend Analysis:** Privacy-friendly tools are used to identify usage patterns and employ techniques like hierarchical summarization to detect large-scale misuse, such as coordinated influence campaigns. * **Proactive Threat Hunting:** The team actively searches for new threats by analyzing data and monitoring online forums frequented by malicious actors. ### Collaboration and Future Outlook: Anthropic acknowledges that AI safety is a shared responsibility and actively collaborates with researchers, policymakers, and the public to develop robust safeguards. The report also highlights related events and resources for learning more about AI and big data, including the AI & Big Data Expo and other enterprise technology events.

Anthropic details its AI safety strategy

Read original at AI News

Anthropic has detailed its safety strategy to try and keep its popular AI model, Claude, helpful while avoiding perpetuating harms.Central to this effort is Anthropic’s Safeguards team; who aren’t your average tech support group, they’re a mix of policy experts, data scientists, engineers, and threat analysts who know how bad actors think.

However, Anthropic’s approach to safety isn’t a single wall but more like a castle with multiple layers of defence. It all starts with creating the right rules and ends with hunting down new threats in the wild.First up is the Usage Policy, which is basically the rulebook for how Claude should and shouldn’t be used.

It gives clear guidance on big issues like election integrity and child safety, and also on using Claude responsibly in sensitive fields like finance or healthcare.To shape these rules, the team uses a Unified Harm Framework. This helps them think through any potential negative impacts, from physical and psychological to economic and societal harm.

It’s less of a formal grading system and more of a structured way to weigh the risks when making decisions. They also bring in outside experts for Policy Vulnerability Tests. These specialists in areas like terrorism and child safety try to “break” Claude with tough questions to see where the weaknesses are.

We saw this in action during the 2024 US elections. After working with the Institute for Strategic Dialogue, Anthropic realised Claude might give out old voting information. So, they added a banner that pointed users to TurboVote, a reliable source for up-to-date, non-partisan election info.Teaching Claude right from wrongThe Anthropic Safeguards team works closely with the developers who train Claude to build safety from the start.

This means deciding what kinds of things Claude should and shouldn’t do, and embedding those values into the model itself.They also team up with specialists to get this right. For example, by partnering with ThroughLine, a crisis support leader, they’ve taught Claude how to handle sensitive conversations about mental health and self-harm with care, rather than just refusing to talk.

This careful training is why Claude will turn down requests to help with illegal activities, write malicious code, or create scams.Before any new version of Claude goes live, it’s put through its paces with three key types of evaluation.Safety evaluations: These tests check if Claude sticks to the rules, even in tricky, long conversations.

Risk assessments: For really high-stakes areas like cyber threats or biological risks, the team does specialised testing, often with help from government and industry partners.Bias evaluations: This is all about fairness. They check if Claude gives reliable and accurate answers for everyone, testing for political bias or skewed responses based on things like gender or race.

This intense testing helps the team see if the training has stuck and tells them if they need to build extra protections before launch.(Credit: Anthropic)Anthropic’s never-sleeping AI safety strategyOnce Claude is out in the world, a mix of automated systems and human reviewers keep an eye out for trouble.

The main tool here is a set of specialised Claude models called “classifiers” that are trained to spot specific policy violations in real-time as they happen.If a classifier spots a problem, it can trigger different actions. It might steer Claude’s response away from generating something harmful, like spam.

For repeat offenders, the team might issue warnings or even shut down the account.The team also looks at the bigger picture. They use privacy-friendly tools to spot trends in how Claude is being used and employ techniques like hierarchical summarisation to spot large-scale misuse, such as coordinated influence campaigns.

They are constantly hunting for new threats, digging through data, and monitoring forums where bad actors might hang out.However, Anthropic says it knows that ensuring AI safety isn’t a job they can do alone. They’re actively working with researchers, policymakers, and the public to build the best safeguards possible.

(Lead image by Nick Fewings)See also: Suvianna Grecu, AI for Change: Without rules, AI risks ‘trust crisis’Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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