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aria-label="Try Claude"><span>Try Claude</span></a><button class="SiteHeader_mobileIcon__OK1HE" aria-label="Navigation menu"><svg class="Icon_icon__UdTNj" width="24" height="24" viewBox="0 0 40 40"><path d="M5.418 25.375v-2.083h29.166v2.083H5.418Zm0-8.667v-2.083h29.166v2.083H5.418Z" fill="#141413"></path></svg></button></div></div></header><main id="main-content" class=""><article class="LegalPageDetail_legal-page___xgf0"><div><div class="page-wrapper s:grid s:grid-12"><header class="xs:col-start-2 xs:col-span-10 s:col-start-3 s:col-span-8"><h1 class="LegalPageDetail_title__I0pkv h1 spacer-medium-mb">Usage Policy</h1><div class="LegalPageDetail_metadata__PP2lH"><span class="LegalPageDetail_effective-date__ZhvLf text-label">Effective June 6, 2024</span><a class="text-label" href="/legal/archive/89e829b9-91a1-44e1-8915-d08ce3007cfe">Previous Version</a></div></header><div class="LegalPageDetail_body__AK5Wr xs:col-start-2 xs:col-span-10 s:col-start-3 s:col-span-8 text-b2"><p>Our Usage Policy (also referred to as our “Acceptable Use Policy” or “AUP”) applies to anyone who uses Anthropic’s products and services, and is intended to help our users stay safe and ensure our products and services are being used responsibly.</p><p></p><p>The Usage Policy is categorized according to who can use our products and for what purposes. We will update our policy as our technology and the associated risks evolve or as we learn about unanticipated risks from our users.</p><p></p><ul><li><strong>Universal Usage Standards: </strong>Our Universal Usage Standards apply to all users including individuals, developers, and businesses.</li><li><strong>High-Risk Use Case Requirements</strong>: Our High-Risk Use Case Requirements apply to specific use cases that pose an elevated risk of harm.</li><li><strong>Disclosure Requirements: </strong>Our Disclosure Requirements apply to specific use cases where it is especially important for users to understand that they are interacting with an AI system.</li></ul><p>Anthropic’s Trust and Safety Team will implement detections and monitoring to enforce our Usage Policies so please review these policies carefully before using our products. If we learn that you have violated our Usage Policy, we may throttle, suspend, or terminate your access to our products and services. If you discover that our model outputs are inaccurate, biased or harmful, please notify us at <a href="mailto:usersafety@anthropic.com">usersafety@anthropic.com</a> or report it directly in the product through the “report issues” thumbs down button. You can read more about our Trust and Safety practices and recommendations in our <a href="https://support.anthropic.com/en/collections/4078535-trust-safety">T&S Support Center</a>.</p><p>This Usage Policy is calibrated to strike an optimal balance between enabling beneficial uses and mitigating potential harms. Anthropic may enter into contracts with certain governmental customers that tailor use restrictions to that customer’s public mission and legal authorities if, in Anthropic’s judgment, the contractual use restrictions and applicable safeguards are adequate to mitigate the potential harms addressed by this Usage Policy.</p><h3>Universal Usage Standards</h3><p><strong>Do Not Compromise Children’s Safety</strong></p><p>This includes using our products or services to:</p><ul><li>Create, distribute, or promote child sexual abuse material. We strictly prohibit and will report to relevant authorities and organizations where appropriate any content that exploits or abuses minors</li><li>Facilitate the trafficking, sextortion, or any other form of exploitation of a minor</li><li>Facilitate minor grooming, including generating content designed to impersonate a minor</li><li>Facilitate or depict child abuse of any form, including instructions for how to conceal abuse</li><li>Promote or facilitate pedophilic relationships, including via roleplay with the model</li><li>Fetishize minors</li></ul><p><strong>Do Not Compromise Critical Infrastructure</strong></p><p>This includes using our products or services to:</p><ul><li>Facilitate the destruction or disruption of critical infrastructure such as power grids, water treatment facilities, telecommunication networks, or air traffic control systems</li><li>Obtain unauthorized access to critical systems such as voting machines, healthcare databases, and financial markets</li><li>Interfere with the operation of military bases and related infrastructure</li></ul><p><strong>Do Not Incite Violence or Hateful Behavior</strong></p><p>This includes using our products or services to:</p><ul><li>Incite, facilitate, or promote violent extremism, terrorism, or hateful behavior</li><li>Depict support for organizations or individuals associated with violent extremism, terrorism, or hateful behavior</li><li>Facilitate or promote any act of violence or intimidation targeting individuals, groups, animals, or property</li><li>Promote discriminatory practices or behaviors against individuals or groups on the basis of one or more protected attributes such as race, ethnicity, religion, nationality, gender, sexual orientation, or any other identifying trait</li></ul><p><strong>Do Not Compromise Someone’s Privacy or Identity</strong></p><p>This includes using our products or services to:</p><ul><li>Compromise security or gain unauthorized access to computer systems or networks, including spoofing and social engineering</li><li>Violate the security, integrity, or availability of any user, network, computer, device, or communications system, software application, or network or computing device</li><li>Violate any person's privacy rights as defined by applicable privacy laws, such as sharing personal information without consent, accessing private data unlawfully, or violating any relevant privacy regulations</li><li>Misuse, collect, solicit, or gain access to private information without permission such as non-public contact details, health data, biometric or neural data (including facial recognition), or confidential or proprietary data</li><li>Impersonate a human by presenting results as human-generated, or using results in a manner intended to convince a natural person that they are communicating with a natural person when they are not</li></ul><p><strong>Do Not Create or Facilitate the Exchange of Illegal or Highly Regulated Weapons or Goods</strong></p><p>This includes using our products or services to:</p><ul><li>Produce, modify, design, market, or distribute weapons, explosives, dangerous materials or other systems designed to cause harm to or loss of human life</li><li>Engage in or facilitate any illegal activity, such as the use, acquisition, or exchange of illegal and controlled substances, or the facilitation of human trafficking and prostitution</li></ul><p><strong>Do Not Create Psychologically or Emotionally Harmful</strong> <strong>Content</strong></p><p>This includes using our products or services to:</p><ul><li>Facilitate or conceal any form of self-harm, including disordered eating and unhealthy or compulsive exercise</li><li>Engage in behaviors that promote unhealthy or unattainable body image or beauty standards, such as using the model to critique anyone’s body shape or size</li><li>Shame, humiliate, intimidate, bully, harass, or celebrate the suffering of individuals</li><li>Coordinate the harassment or intimidation of an individual or group</li><li>Generate content depicting sexual violence</li><li>Generate content depicting animal cruelty or abuse</li><li>Generate violent or gory content that is inspired by real acts of violence</li><li>Promote, trivialize, or depict graphic violence or gratuitous gore</li><li>Develop a product, or support an existing service that facilitates deceptive techniques with the intent of causing emotional harm</li></ul><p><strong>Do Not Spread Misinformation</strong></p><p>This includes the usage of our products or services to:</p><ul><li>Create and disseminate deceptive or misleading information about a group, entity or person</li><li>Create and disseminate deceptive or misleading information about laws, regulations, procedures, practices, standards established by an institution, entity or governing body</li><li>Create and disseminate deceptive or misleading information with the intention of targeting specific groups or persons with the misleading content</li><li>Create and advance conspiratorial narratives meant to target a specific group, individual or entity</li><li>Impersonate real entities or create fake personas to falsely attribute content or mislead others about its origin without consent or legal right</li><li>Provide false or misleading information related to medical, health or science issues</li></ul><p><strong>Do Not Create Political Campaigns or Interfere in Elections</strong></p><p>This includes the usage of our products or services to:</p><ul><li>Promote or advocate for a particular political candidate, party, issue or position. This includes soliciting votes, financial contributions, or public support for a political entity</li><li>Engage in political lobbying to actively influence the decisions of government officials, legislators, or regulatory agencies on legislative, regulatory, or policy matters. This includes advocacy or direct communication with officials or campaigns to sway public opinion on specific legislation or policies</li><li>Engage in campaigns, including political campaigns, that promote false or misleading information to discredit or undermine individuals, groups, entities or institutions</li><li>Incite, glorify or facilitate the disruption of electoral or civic processes, such as targeting voting machines, or obstructing the counting or certification of votes</li><li>Generate false or misleading information on election laws, procedures and security, candidate information, how to participate, or discouraging participation in an election</li></ul><p><strong>Do Not Use for Criminal Justice, Law Enforcement, Censorship or Surveillance Purposes</strong></p><p>This includes the usage of our products or services to:</p><ul><li>Make determinations on criminal justice applications, including making decisions about or determining eligibility for parole or sentencing</li><li>Target or track a person’s physical location, emotional state, or communication without their consent, including using our products for facial recognition, battlefield management applications or predictive policing</li><li>Utilize Claude to assign scores or ratings to individuals based on an assessment of their trustworthiness or social behavior</li><li>Build or support emotional recognition systems or techniques that are used to infer people’s emotions</li><li>Analyze or identify specific content to censor on behalf of a government organization</li><li>Utilize Claude as part of any biometric categorization system for categorizing people based on their biometric data to infer their race, political opinions, trade union membership, religious or philosophical beliefs, sex life or sexual orientation</li><li>Use the model for any official local, state or national law enforcement application<strong>.</strong> Except for the following permitted applications by law enforcement organizations:<ul><li>Back office uses including internal training, call center support, document summarization, and accounting;</li><li>Analysis of data for the location of missing persons, including in human trafficking cases, and other related applications, provided that such applications do not otherwise violate or impair the liberty, civil liberties, or human rights of natural persons</li></ul></li></ul><p><strong>Do Not Engage in Fraudulent, Abusive, or Predatory Practices</strong></p><p>This includes using our products or services to:</p><ul><li>Facilitate the production, acquisition, or distribution of counterfeit or illicitly acquired goods</li><li>Promote or facilitate the generation or distribution of spam</li><li>Generate content for fraudulent activities, schemes, scams, phishing, or malware that can result in direct financial or psychological harm</li><li>Generate content for the purposes of developing or promoting the sale or distribution of fraudulent or deceptive products</li><li>Generate deceptive or misleading digital content such as fake reviews, comments, or media</li><li>Engage in or facilitate multi-level marketing, pyramid schemes, or other deceptive business models that use high-pressure sales tactics or exploit participants</li><li>Promote or facilitate payday loans, title loans, or other high-interest, short-term lending practices that exploit vulnerable individuals</li><li>Engage in deceptive, abusive behaviors, practices, or campaigns that exploits people due to their age, disability or a specific social or economic situation</li><li>Promote or facilitate the use of abusive or harassing debt collection practices</li><li>Develop a product, or support an existing service that deploys subliminal, manipulative, or deceptive techniques to distort behavior by impairing decision-making</li><li>Plagiarize or engage in academic dishonesty</li></ul><p><strong>Do Not Abuse our Platform</strong></p><p>This includes using our products or services to:</p><ul><li>Coordinate malicious activity across multiple accounts such as creating multiple accounts to avoid detection or circumvent product guardrails or generating identical or similar prompts that otherwise violate our Usage Policy</li><li>Utilize automation in account creation or to engage in spammy behavior</li><li>Circumvent a ban through the use of a different account, such as the creation of a new account, use of an existing account, or providing access to a person or entity that was previously banned</li><li>Facilitate or provide account access to Claude to persons or entities who are located in unsupported locations</li><li>Intentionally bypass capabilities or restrictions established within our products for the purposes of instructing the model to produce harmful outputs (e.g., jailbreaking or prompt injection) without an authorized use-case approved by Anthropic</li><li>Unauthorized utilization of prompts and completions to train an AI model (e.g., “model scraping”)</li></ul><p><strong>Do Not Generate Sexually Explicit Content</strong></p><p>This includes the usage of our products or services to:</p><ul><li>Depict or request sexual intercourse or sex acts</li><li>Generate content related to sexual fetishes or fantasies</li><li>Facilitate, promote, or depict incest or bestiality</li><li>Engage in erotic chats</li></ul><h3>High-Risk Use Case Requirements</h3><p>Some integrations (meaning use cases involving the use of our products and services) pose an elevated risk of harm because they influence domains that are vital to public welfare and social equity. “High-Risk Use Cases” include:</p><p></p><ul><li><strong>Legal:</strong> Integrations related to legal interpretation, legal guidance, or decisions with legal implications</li><li><strong>Healthcare: </strong>Integrations affecting healthcare decisions, medical diagnosis, patient care, or medical guidance. Wellness advice (e.g., advice on sleep, stress, nutrition, exercise, etc.) does not fall under this category</li><li><strong>Insurance:</strong> Integrations related to health, life, property, disability, or other types of insurance underwriting, claims processing, or coverage decisions</li><li><strong>Finance:</strong> Integrations related to financial decisions, including investment advice, loan approvals, and determining financial eligibility or creditworthiness</li><li><strong>Employment and housing:</strong> Integrations related to decisions about the employability of individuals, resume screening, hiring tools, or other employment determinations or decisions regarding eligibility for housing, including leases and home loans</li><li><strong>Academic testing, accreditation and admissions:</strong> Integrations related to standardized testing companies that administer school admissions (including evaluating, scoring or ranking prospective students), language proficiency, or professional certification exams; agencies that evaluate and certify educational institutions.</li><li><strong>Media or professional journalistic content:</strong> Integrations related to using our products or services to automatically generate content and publish it for external consumption</li></ul><p></p><p>If your integration is listed above, we require that you implement the additional safety measures listed below:</p><ul><li><strong>Human-in-the-loop: </strong>when using our products or services to provide advice, recommendations, or subjective decisions that directly impact individuals in high-risk domains, a qualified professional in that field must review the content or decision prior to dissemination or finalization. This requirement applies specifically to content or decisions that are provided to consumers or the general public, or decisions made about an individual. Your business is responsible for the accuracy and appropriateness of that information. For other types of content generation or interactions with users that do not involve direct advice, recommendations, or subjective decisions, human review is strongly encouraged but not mandatory.</li><li><strong>Disclosure:</strong> you must disclose to your customers or end users that you are using our services to help inform your decisions or recommendations.</li></ul><p></p><p><strong>Disclosure Requirements</strong></p><p>Finally, the below use cases – regardless of whether they are High Risk Use Cases – must disclose to their users that they are interacting with an AI system rather than a human:</p><ul><li><strong>All customer-facing chatbots </strong>including any external-facing or interactive AI agent</li><li><strong>Products serving minors:</strong> Organizations providing minors with the ability to directly interact with products that incorporate our API(s). <strong>Note:</strong> These organizations must also comply with the additional guidelines outlined in our <a 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Wellness advice (e.g., advice on sleep, stress, nutrition, exercise, etc.) does not fall under this category\",\"_key\":\"332dfe7ebb5d1\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c8832a27eaa0\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"37dd6685990d\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Insurance:\",\"_key\":\"0d8735455ab80\"},{\"text\":\" Integrations related to health, life, property, disability, or other types of insurance underwriting, claims processing, or coverage decisions\",\"_key\":\"0d8735455ab81\",\"_type\":\"span\",\"marks\":[]}],\"level\":1},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Finance:\",\"_key\":\"7d21422c0eb30\"},{\"text\":\" Integrations related to financial decisions, including investment advice, loan approvals, and determining financial eligibility or creditworthiness\",\"_key\":\"7d21422c0eb31\",\"_type\":\"span\",\"marks\":[]}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"5640197374bf\",\"listItem\":\"bullet\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Employment and housing:\",\"_key\":\"d38a89f63f0b0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Integrations related to decisions about the employability of individuals, resume screening, hiring tools, or other employment determinations or decisions regarding eligibility for housing, including leases and home loans\",\"_key\":\"d38a89f63f0b1\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"539e2775eaf6\",\"listItem\":\"bullet\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Academic testing, accreditation and admissions:\",\"_key\":\"f784fad3ff430\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Integrations related to standardized testing companies that administer school admissions (including evaluating, scoring or ranking prospective students), language proficiency, or professional certification exams; agencies that evaluate and certify educational institutions.\",\"_key\":\"f784fad3ff431\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"a6ab2c9e6bfb\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Media or professional journalistic content:\",\"_key\":\"ade37f32d63a0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Integrations related to using our products or services to automatically generate content and publish it for external consumption\",\"_key\":\"ade37f32d63a1\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9a744198979a\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"\",\"_key\":\"fe17c22e7fd50\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9e2bdf95db9b\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"2c51d0041047\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"If your integration is listed above, we require that you implement the additional safety measures listed below:\",\"_key\":\"c3f2de2593010\"}]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Human-in-the-loop: \",\"_key\":\"ab6feef775a50\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"when using our products or services to provide advice, recommendations, or subjective decisions that directly impact individuals in high-risk domains, a qualified professional in that field must review the content or decision prior to dissemination or finalization. This requirement applies specifically to content or decisions that are provided to consumers or the general public, or decisions made about an individual. Your business is responsible for the accuracy and appropriateness of that information. For other types of content generation or interactions with users that do not involve direct advice, recommendations, or subjective decisions, human review is strongly encouraged but not mandatory.\",\"_key\":\"ab6feef775a51\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"28b3b572ebd1\",\"listItem\":\"bullet\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"f8d5ef63fcc7\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Disclosure:\",\"_key\":\"9b108816848b0\"},{\"marks\":[],\"text\":\" you must disclose to your customers or end users that you are using our services to help inform your decisions or recommendations.\",\"_key\":\"9b108816848b1\",\"_type\":\"span\"}],\"level\":1},{\"markDefs\":[],\"children\":[{\"text\":\"\",\"_key\":\"43a53bb4caeb0\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"40b28ec888c7\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"cce05c4820a3\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Disclosure Requirements\",\"_key\":\"b61bbc740ee10\"}]},{\"_key\":\"8edeb73b188f\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Finally, the below use cases – regardless of whether they are High Risk Use Cases – must disclose to their users that they are interacting with an AI system rather than a human:\",\"_key\":\"0b4f0a9319680\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"3ad6d9d0104b\",\"listItem\":\"bullet\",\"markDefs\":[{\"href\":\"https://docs.google.com/document/d/1eSvL31wPlTXzRbMN4B9PxzelrSLyArNhylH14QFMfR8/edit\",\"_key\":\"3d12b02ef861\",\"_type\":\"link\"}],\"children\":[{\"text\":\"All customer-facing chatbots \",\"_key\":\"9a556a0ae9000\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\"including any external-facing or interactive AI agent\",\"_key\":\"9a556a0ae9001\"}]},{\"_key\":\"189b45a484c6\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://support.anthropic.com/en/articles/9307344-responsible-use-of-anthropic-s-models-guidelines-for-organizations-serving-minors\",\"_key\":\"3d12b02ef861\"}],\"children\":[{\"text\":\"Products serving minors:\",\"_key\":\"4265aba6d2600\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\" Organizations providing minors with the ability to directly interact with products that incorporate our API(s). \",\"_key\":\"4265aba6d2601\"},{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Note:\",\"_key\":\"4265aba6d2602\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" These organizations must also comply with the additional guidelines outlined in our \",\"_key\":\"4265aba6d2603\"},{\"_type\":\"span\",\"marks\":[\"3d12b02ef861\"],\"text\":\"Help Center article\",\"_key\":\"4265aba6d2604\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\"}],\"translations\":[{\"language\":{\"_type\":\"tag\",\"label\":\"Portuguese\",\"_key\":\"pt\",\"value\":\"pt\"},\"_key\":\"ae12d6b815ef\",\"body\":[{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Política de Uso\",\"_key\":\"e0de4d38c4f90\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"542862cfe13b\",\"markDefs\":[]},{\"_key\":\"edb47f9b19a0\",\"markDefs\":[],\"children\":[{\"text\":\"\",\"_key\":\"f86612bda5c00\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\"},{\"style\":\"normal\",\"_key\":\"03ca675db33e\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Data da última atualização: 06 de junho de 2024\",\"_key\":\"dee6850f678b0\"}],\"_type\":\"block\"},{\"_key\":\"fe5058aafe45\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"_______________________________________________________________________\",\"_key\":\"7483be9b8d4c0\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"text\":\"Nossa Política de Uso (também referida como \\\"Política de Uso Aceitável\\\" ou \\\"PUA\\\") aplica-se a qualquer pessoa que utilize os produtos e serviços da Anthropic e visa ajudar nossos usuários a permanecerem seguros e garantir que nossos produtos e serviços sejam utilizados de maneira responsável.\",\"_key\":\"7cb12b11d0ed0\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"14e71c0e64e8\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"A Política de Uso é categorizada de acordo com quem pode usar nossos produtos e para quais finalidades. Atualizaremos nossa política à medida que nossa tecnologia e os riscos associados evoluam, ou conforme aprendemos sobre riscos imprevistos a partir de nossos usuários.\",\"_key\":\"e9ca6bd88e2d0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"807e4fe151d8\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Padrões Universais de Uso:\",\"_key\":\"2fdfad154a4d0\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"marks\":[],\"text\":\" Nossos Padrões Universais de Uso aplicam-se a todos os usuários, incluindo indivíduos, desenvolvedores e empresas.\",\"_key\":\"2fdfad154a4d1\",\"_type\":\"span\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"4e0613d8a7f4\"},{\"markDefs\":[],\"children\":[{\"marks\":[\"strong\"],\"text\":\"Requisitos para Casos de\",\"_key\":\"c64937df6c5b0\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" \",\"_key\":\"c64937df6c5b1\"},{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Uso de Alto Risco:\",\"_key\":\"c64937df6c5b2\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Nossos Requisitos para Casos de Uso de Alto Risco aplicam-se a casos específicos que representam um risco elevado de dano.\",\"_key\":\"c64937df6c5b3\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"cc1bcbf1b631\",\"listItem\":\"bullet\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Requisitos de Divulgação:\",\"_key\":\"67f579b37f380\"},{\"marks\":[],\"text\":\" Nossos Requisitos de Divulgação aplicam-se a casos específicos onde é especialmente importante que os usuários compreendam que estão interagindo com um sistema de IA.\",\"_key\":\"67f579b37f381\",\"_type\":\"span\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"d23401c0776f\",\"listItem\":\"bullet\",\"markDefs\":[]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"mailto:usersafety@anthropic.com\",\"_key\":\"91fc7a5eda28\"},{\"_key\":\"c39021b2e13d\",\"_type\":\"link\",\"href\":\"https://support.anthropic.com/en/collections/4078535-trust-safety\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"A Equipe de Confiança e Segurança da Anthropic implementará detecções e monitoramento para fazer cumprir nossas Políticas de Uso, então por favor revise estas políticas cuidadosamente antes de utilizar nossos produtos. Se descobrirmos que você violou nossa Política de Uso, podemos limitar, suspender ou encerrar seu acesso aos nossos produtos e serviços. Se você entender que os resultados do nosso modelo são imprecisos, tendenciosos ou prejudiciais, por favor nos notifique em \",\"_key\":\"03ee26d35c3f0\"},{\"_type\":\"span\",\"marks\":[\"91fc7a5eda28\"],\"text\":\"usersafety@anthropic.com\",\"_key\":\"03ee26d35c3f1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" ou notifique diretamente no produto através do botão \\\"notificar problemas\\\". Você pode ler mais sobre nossas práticas e recomendações de Confiança e Segurança em nosso \",\"_key\":\"03ee26d35c3f2\"},{\"marks\":[\"c39021b2e13d\"],\"text\":\"Centro de Suporte de Confiança e Segurança\",\"_key\":\"03ee26d35c3f3\",\"_type\":\"span\"},{\"marks\":[],\"text\":\".\",\"_key\":\"03ee26d35c3f4\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b4fefbf5ce4e\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Esta Política de Uso é projetada para atingir um equilíbrio ideal entre permitir usos benéficos e mitigar potenciais danos. A Anthropic poderá celebrar contratos com determinados clientes governamentais que adaptam as restrições de uso conforme a missão pública desses clientes e as autoridades legais se, na avaliação da Anthropic, as restrições de uso contratuais e salvaguardas aplicáveis forem adequadas para mitigar os potenciais danos abordados por esta Política de Uso.\",\"_key\":\"0f2f608011c10\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ae78e28e28be\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Padrões Universais de Uso\",\"_key\":\"9d03880d97e30\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"fef5b72a0195\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Não Comprometa a Segurança das Crianças\",\"_key\":\"98c100de7e400\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"5a86f880a454\",\"markDefs\":[]},{\"style\":\"normal\",\"_key\":\"db6d853e262b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Isso inclui usar nossos produtos ou serviços para:\",\"_key\":\"844b823ea9350\"}],\"_type\":\"block\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Criar, distribuir ou promover material de abuso sexual infantil. Nós estritamente proibimos e denunciaremos às autoridades e organizações relevantes, quando apropriado, qualquer conteúdo que explore ou abuse menores.\",\"_key\":\"bda5c80221200\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"dfac2fd93642\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Facilitar o tráfico, extorsão sexual ou qualquer outra forma de exploração de menores.\",\"_key\":\"a108bb570e0e0\",\"_type\":\"span\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"670271b34921\"},{\"style\":\"normal\",\"_key\":\"55ada5e1965e\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Facilitar o aliciamento de menores, incluindo gerar conteúdo para se passar por um 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pelas leis de privacidade aplicáveis, como compartilhar informações pessoais sem consentimento, acessar dados privados ilegalmente ou violar quaisquer regulamentações de privacidade relevantes.\",\"_key\":\"000d0be2d4bc0\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"0f3a325e7030\"},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"4b9d3f9ec6c7\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Utilizar indevidamente, coletar, solicitar ou obter acesso a informações privadas sem permissão, como detalhes de contato não públicos, dados de saúde, dados biométricos ou neurais (incluindo reconhecimento facial), ou dados confidenciais ou proprietários.\",\"_key\":\"cb0d0022d73a0\",\"_type\":\"span\",\"marks\":[]}]},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"22441f0cc3f6\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Passar-se por humano ao apresentar resultados como 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Conselhos de bem-estar (por exemplo, conselhos sobre sono, estresse, nutrição, exercícios etc.) não se enquadram nesta categoria.\",\"_key\":\"ef580d0a8fcd1\",\"_type\":\"span\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"f37fe2bbafa7\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Seguros\",\"_key\":\"a3089ac634050\"},{\"_type\":\"span\",\"marks\":[],\"text\":\": Integrações relacionadas à subscrição, processamento de sinistros ou decisões de cobertura de seguros de saúde, vida, propriedade, invalidez ou outros tipos de seguros.\",\"_key\":\"a3089ac634051\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"bbd0dd971b75\",\"listItem\":\"bullet\",\"markDefs\":[]},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"f810c39e3721\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"marks\":[\"strong\"],\"text\":\"Finanças: \",\"_key\":\"99375b176af60\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Integrações relacionadas a decisões financeiras, incluindo conselhos de investimento, aprovações de empréstimos e determinação de elegibilidade financeira ou solvência.\",\"_key\":\"99375b176af61\"}],\"level\":1},{\"_key\":\"5adc0e3a7067\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Emprego e moradia:\",\"_key\":\"fb92ac5e692a0\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\" Integrações relacionadas a decisões sobre a empregabilidade de indivíduos, triagem de currículos, ferramentas de contratação ou outras determinações de emprego ou decisões sobre elegibilidade para moradia, incluindo locações e empréstimos residenciais.\",\"_key\":\"fb92ac5e692a1\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"_key\":\"518b07cb78f70\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Testes acadêmicos, credenciamento e admissões:\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Integrações relacionadas a empresas de testes padronizados que administram admissões escolares (incluindo avaliação, pontuação ou classificação de estudantes prospectivos), proficiência em idiomas ou exames de certificação profissional; agências que avaliam e certificam instituições educacionais.\",\"_key\":\"518b07cb78f71\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"07855575687e\",\"listItem\":\"bullet\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Conteúdo midiático ou jornalístico profissional:\",\"_key\":\"d0c1e84fb9380\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Integrações relacionadas ao uso de nossos produtos ou serviços para gerar automaticamente conteúdo e publicá-lo para consumo externo.\",\"_key\":\"d0c1e84fb9381\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"931a73b42f40\",\"listItem\":\"bullet\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Se sua integração estiver listada acima, exigimos que você implemente as medidas adicionais de segurança listadas abaixo:\",\"_key\":\"8a5cf08619660\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"46703f0ed1c5\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Interação com Humanos no Processo:\",\"_key\":\"ae6629fa38090\"},{\"_key\":\"ae6629fa38091\",\"_type\":\"span\",\"marks\":[],\"text\":\" Ao usar nossos produtos ou serviços para fornecer conselhos, recomendações ou decisões subjetivas que impactem diretamente indivíduos em domínios de alto risco, um profissional qualificado nesse campo deve revisar o conteúdo ou decisão antes da disseminação ou finalização. Este requisito se aplica especificamente a conteúdos ou decisões fornecidas a consumidores ou ao público em geral, ou decisões tomadas sobre um indivíduo. Sua empresa é responsável pela precisão e adequação dessas informações. Para outros tipos de geração de conteúdo ou interações com usuários que não envolvam aconselhamento direto, recomendações ou decisões subjetivas, a revisão humana é fortemente encorajada, mas não obrigatória.\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"cb41d07b5c72\",\"listItem\":\"bullet\"},{\"style\":\"normal\",\"_key\":\"5b8c2cc337fb\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Divulgação:\",\"_key\":\"9acb6e9bb84c0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Você deve informar aos seus clientes ou usuários finais que está usando nossos serviços para auxiliar em suas decisões ou recomendações.\",\"_key\":\"9acb6e9bb84c1\"}],\"level\":1,\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"15a3ea7e56a2\",\"markDefs\":[],\"children\":[{\"_key\":\"91f5efe798a00\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Requisitos de Divulgação:\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" Finalmente, os casos de uso abaixo - independentemente de serem ou não Casos de Uso de Alto Risco - devem informar aos seus usuários que estão interagindo com um sistema de IA e não com um ser humano:\",\"_key\":\"91f5efe798a01\"}],\"_type\":\"block\"},{\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"03afa0130afa\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Todos os chatbots voltados para o cliente\",\"_key\":\"19cce98e04d90\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\", incluindo qualquer agente de IA externo ou interativo.\",\"_key\":\"19cce98e04d91\"}]},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Produtos que atendem menores de idade:\",\"_key\":\"9ddf7d3779880\"},{\"text\":\" Entidades que permitem que menores interajam diretamente com produtos que incorporam nossas APIs. 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Our model card shows comparisons to models that have been announced but not yet released, such as Gemini 1.5 Pro. In addition, we’d like to note that engineers have worked to optimize prompts and few-shot samples for evaluations and reported higher scores for a newer GPT-4T model. \",\"_key\":\"137b54da87990\"},{\"_key\":\"b4ba8cc2e443\",\"_type\":\"span\",\"marks\":[\"b3d47055ad85\"],\"text\":\"Source\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\",\"_key\":\"04f51fedd06a\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"95b520db64f4\",\"listItem\":\"number\"}],\"_rev\":\"XBP1YM482O189X05gwOKSe\",\"body\":[{\"markDefs\":[],\"children\":[{\"_key\":\"e3f6ee680e7d0\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"e9ca761c9e34\"},{\"style\":\"inline\",\"_key\":\"8fe584460916\",\"asset\":{\"_ref\":\"image-4e78f69ef8d4186fb5691714abe36224483d91b0-2880x1620-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"description\":\"Claude 3 \"},{\"markDefs\":[{\"_key\":\"e690c5685704\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/api#pricing\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Today, we're announcing the Claude 3 model family, which sets new industry benchmarks across a wide range of cognitive tasks. The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and \",\"_key\":\"6c7c68493bd10\"},{\"_type\":\"span\",\"marks\":[\"e690c5685704\"],\"text\":\"cost\",\"_key\":\"1309100e4264\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" for their specific application.\",\"_key\":\"443337375366\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"d4714c0f2d48\"},{\"style\":\"normal\",\"_key\":\"496fb9c9f406\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/supported-countries\",\"_key\":\"b2d9865719c6\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Opus and Sonnet are now available to use in claude.ai and the Claude API which is now generally available in \",\"_key\":\"b0e67afac1800\"},{\"marks\":[\"b2d9865719c6\"],\"text\":\"159 countries\",\"_key\":\"cfd556a17bfc\",\"_type\":\"span\"},{\"_key\":\"c2f391fd5235\",\"_type\":\"span\",\"marks\":[],\"text\":\". Haiku will be available soon.\"}],\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"a530c9ea6fe9\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"\",\"_key\":\"799454141bb4\",\"_type\":\"span\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Claude 3 model family\",\"_key\":\"59528945df81\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"2889706d30e0\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"2f4d381f922a\",\"asset\":{\"_ref\":\"image-5d20371eeb8d045465bb22cacfd269b5958b004d-2200x1174-png\",\"_type\":\"reference\"}},{\"style\":\"h3\",\"_key\":\"34dbfdf7ee0b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"A new standard for intelligence\",\"_key\":\"7c88ca38cc48\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"text\":\"Opus, our most intelligent model, outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.\",\"_key\":\"b2c1b6abdeda0\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"6d478b0f5b1a\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"791ebb9539f9\",\"markDefs\":[{\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"aca6e529fda4\",\"_type\":\"link\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"All \",\"_key\":\"ea2185156be50\"},{\"marks\":[\"aca6e529fda4\"],\"text\":\"Claude 3\",\"_key\":\"f41e11b72a5a\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" models show increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French.\",\"_key\":\"ce9cf674fb4f\"}]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Below is a comparison of the Claude 3 models to those of our peers on multiple benchmarks [1] of capability:\",\"_key\":\"e66d47cf953f0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b77f3b42f058\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"96b31e47a780\",\"asset\":{\"_ref\":\"image-9ad98d612086fe52b3042f9183414669b4d2a3da-2200x1954-png\",\"_type\":\"reference\"}},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"122d99955c94\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Near-instant results\",\"_key\":\"94ebd918295e0\"}]},{\"_key\":\"a716760d26f9\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 models can power live customer chats, auto-completions, and data extraction tasks where responses must be immediate and in real-time.\",\"_key\":\"d636443bda240\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Haiku is the fastest and most cost-effective model on the market for its intelligence category. It can read an information and data dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds. Following launch, we expect to improve performance even further.\",\"_key\":\"c30fe08114e00\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ef96ccac31b1\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"2c59a54bb9cf\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 with higher levels of intelligence. It excels at tasks demanding rapid responses, like knowledge retrieval or sales automation. Opus delivers similar speeds to Claude 2 and 2.1, but with much higher levels of intelligence.\",\"_key\":\"5d256ef7b4150\"}]},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"118680357dd1\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Strong vision capabilities\",\"_key\":\"e67b4b4e5440\",\"_type\":\"span\"}]},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 models have sophisticated vision capabilities on par with other leading models. They can process a wide range of visual formats, including photos, charts, graphs and technical diagrams. We’re particularly excited to provide this new modality to our enterprise customers, some of whom have up to 50% of their knowledge bases encoded in various formats such as PDFs, flowcharts, or presentation slides.\",\"_key\":\"69e310a95d7b0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"4c7c669b3031\",\"markDefs\":[]},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"5b0b439445a2\",\"asset\":{\"_ref\":\"image-6b66d86ff0c180e95bc6ad2e6e4a1843aa74c80f-2200x960-png\",\"_type\":\"reference\"}},{\"children\":[{\"marks\":[],\"text\":\"Fewer refusals\",\"_key\":\"102fdd5307960\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"e1adb462b46f\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Previous Claude models often made unnecessary refusals that suggested a lack of contextual understanding. We’ve made meaningful progress in this area: Opus, Sonnet, and Haiku are significantly less likely to refuse to answer prompts that border on the system’s guardrails than previous generations of models. As shown below, the Claude 3 models show a more nuanced understanding of requests, recognize real harm, and refuse to answer harmless prompts much less often.\",\"_key\":\"bf8b70935bb90\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"19ea17e89d91\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"a78c6685664b\",\"asset\":{\"_ref\":\"image-d1fbcf3d58ebc2dcd2e98aac995d70bf50cb2e9c-2188x918-png\",\"_type\":\"reference\"}},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"2e52cdedf221\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Improved accuracy\",\"_key\":\"7f14def3c85c\"}]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Businesses of all sizes rely on our models to serve their customers, making it imperative for our model outputs to maintain high accuracy at scale. To assess this, we use a large set of complex, factual questions that target known weaknesses in current models. We categorize the responses into correct answers, incorrect answers (or hallucinations), and admissions of uncertainty, where the model says it doesn’t know the answer instead of providing incorrect information. Compared to Claude 2.1, Opus demonstrates a twofold improvement in accuracy (or correct answers) on these challenging open-ended questions while also exhibiting reduced levels of incorrect answers.\",\"_key\":\"692654be5f0a0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"17889c1cfe91\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"0d0ef8e29c15\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In addition to producing more trustworthy responses, we will soon enable citations in our Claude 3 models so they can point to precise sentences in reference material to verify their answers.\\n\",\"_key\":\"dabf8032cfab0\"}]},{\"asset\":{\"_ref\":\"image-7cb598c6a9fa58c12b77f67ee2067feaac4a2de0-2200x896-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"06407e249d10\"},{\"markDefs\":[],\"children\":[{\"_key\":\"5db36703ea090\",\"_type\":\"span\",\"marks\":[],\"text\":\"Long context and near-perfect recall\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"fcfee2b49205\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"48daeea9bc01\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 family of models will initially offer a 200K context window upon launch. However, all three models are capable of accepting inputs exceeding 1 million tokens and we may make this available to select customers who need enhanced processing power.\",\"_key\":\"69e1354a626c0\"}]},{\"children\":[{\"marks\":[],\"text\":\"To process long context prompts effectively, models require robust recall capabilities. The 'Needle In A Haystack' (NIAH) evaluation measures a model's ability to accurately recall information from a vast corpus of data. We enhanced the robustness of this benchmark by using one of 30 random needle/question pairs per prompt and testing on a diverse crowdsourced corpus of documents. Claude 3 Opus not only achieved near-perfect recall, surpassing 99% accuracy, but in some cases, it even identified the limitations of the evaluation itself by recognizing that the \\\"needle\\\" sentence appeared to be artificially inserted into the original text by a human.\",\"_key\":\"bde3af4ac3670\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7dc25016e60c\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"\",\"_key\":\"f4679e3b7a80\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"0015926d72c6\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"91eb42b8ae41\",\"asset\":{\"_ref\":\"image-d2aa12b60e9c57e7057924bd8878d754c7b3d8e7-2200x1088-png\",\"_type\":\"reference\"}},{\"style\":\"h3\",\"_key\":\"aa397b8752f3\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Responsible design\",\"_key\":\"dd43651c5b1f0\"}],\"_type\":\"block\"},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/constitutional-ai-harmlessness-from-ai-feedback\",\"_key\":\"b608daa539d5\"}],\"children\":[{\"marks\":[],\"text\":\"We’ve developed the Claude 3 family of models to be as trustworthy as they are capable. We have several dedicated teams that track and mitigate a broad spectrum of risks, ranging from misinformation and CSAM to biological misuse, election interference, and autonomous replication skills. We continue to develop methods such as \",\"_key\":\"aa543e64358e0\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"b608daa539d5\"],\"text\":\"Constitutional AI\",\"_key\":\"a30dab63a714\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" that improve the safety and transparency of our models, and have tuned our models to mitigate against privacy issues that could be raised by new modalities.\",\"_key\":\"5b0d68dcb1cf\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9c58cc4307c0\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"538d63deb7c9\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://aclanthology.org/2022.findings-acl.165/\",\"_key\":\"8b8565815b0e\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Addressing biases in increasingly sophisticated models is an ongoing effort and we’ve made strides with this new release. As shown in the model card, Claude 3 shows less biases than our previous models according to the \",\"_key\":\"e6f420b3c2a00\"},{\"_type\":\"span\",\"marks\":[\"8b8565815b0e\"],\"text\":\"Bias Benchmark for Question Answering (BBQ)\",\"_key\":\"9b0d6e529f30\"},{\"marks\":[],\"text\":\". We remain committed to advancing techniques that reduce biases and promote greater neutrality in our models, ensuring they are not skewed towards any particular partisan stance.\",\"_key\":\"838b9d7eaaf5\",\"_type\":\"span\"}]},{\"markDefs\":[{\"href\":\"https://www.anthropic.com/news/anthropics-responsible-scaling-policy\",\"_key\":\"a6acdb8c6c9a\",\"_type\":\"link\"},{\"href\":\"https://www.anthropic.com/news/red-teaming-language-models-to-reduce-harms-methods-scaling-behaviors-and-lessons-learned\",\"_key\":\"41ce6e74280d\",\"_type\":\"link\"},{\"_type\":\"link\",\"href\":\"https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/\",\"_key\":\"bff6073a96fa\"},{\"_type\":\"link\",\"href\":\"https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/\",\"_key\":\"34ede29139ed\"},{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"149416bc7eda\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"While the Claude 3 model family has advanced on key measures of biological knowledge, cyber-related knowledge, and autonomy compared to previous models, it remains at AI Safety Level 2 (ASL-2) per our \",\"_key\":\"758320609d91\"},{\"_type\":\"span\",\"marks\":[\"a6acdb8c6c9a\"],\"text\":\"Responsible Scaling Policy\",\"_key\":\"2c7eb5dc236c\"},{\"text\":\". Our \",\"_key\":\"10f95acda51c\",\"_type\":\"span\",\"marks\":[]},{\"_key\":\"ba6380a89fd3\",\"_type\":\"span\",\"marks\":[\"41ce6e74280d\"],\"text\":\"red teaming\"},{\"text\":\" evaluations (performed in line with our \",\"_key\":\"db279d3786f3\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"bff6073a96fa\"],\"text\":\"White House commitments\",\"_key\":\"1941ee0371ec\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" and the \",\"_key\":\"c1d1c30839be\"},{\"_type\":\"span\",\"marks\":[\"34ede29139ed\"],\"text\":\"2023 US Executive Order\",\"_key\":\"bef6d5b627ce\"},{\"text\":\") have concluded that the models present negligible potential for catastrophic risk at this time. We will continue to carefully monitor future models to assess their proximity to the ASL-3 threshold. 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Key improvements include new capability thresholds to indicate when we will upgrade our safeguards, refined processes for evaluating model capabilities and the adequacy of our safeguards (inspired by \",\"_key\":\"b66b73206ce31\"},{\"_type\":\"span\",\"marks\":[\"a8d027e86aae\"],\"text\":\"safety case methodologies\",\"_key\":\"b66b73206ce32\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"), and new measures for internal governance and external input. By learning from our implementation experiences and drawing on risk management practices used in other high-consequence industries, we aim to better prepare for the rapid pace of AI advancement.\",\"_key\":\"b66b73206ce33\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"style\":\"h3\",\"_key\":\"1c3077afa1f5\",\"markDefs\":[],\"children\":[{\"_key\":\"9405114da2700\",\"_type\":\"span\",\"marks\":[],\"text\":\"The promise and challenge of advanced AI\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"As frontier AI models advance, they have the potential to bring about transformative benefits for our society and economy. AI could accelerate scientific discoveries, revolutionize healthcare, enhance our education system, and create entirely new domains for human creativity and innovation. However, frontier AI systems also present new challenges and risks that warrant careful study and effective safeguards.\",\"_key\":\"a1dcb0091c340\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"750223c4acfb\"},{\"markDefs\":[{\"href\":\"https://www.anthropic.com/news/anthropics-responsible-scaling-policy\",\"_key\":\"4791430b5ec2\",\"_type\":\"link\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In September 2023, we \",\"_key\":\"b75aa93fa2c80\"},{\"_type\":\"span\",\"marks\":[\"4791430b5ec2\"],\"text\":\"released\",\"_key\":\"b75aa93fa2c81\"},{\"marks\":[],\"text\":\" our Responsible Scaling Policy, a framework for managing risks from increasingly capable AI systems. After a year of implementation and learning, we are now sharing a significantly updated version that reflects practical insights and accounts for advancing technological capabilities.\",\"_key\":\"b75aa93fa2c82\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c8b393e2960c\"},{\"style\":\"normal\",\"_key\":\"a1948e4cc873\",\"markDefs\":[{\"_key\":\"ba04e1121891\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/legal/aup\"},{\"_key\":\"32798753cd99\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/research#societal-impacts\"}],\"children\":[{\"marks\":[],\"text\":\"Although this policy focuses on catastrophic risks like the categories listed below, they are not the only risks that we monitor and prepare for. Our \",\"_key\":\"5d488fbe87cd0\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"ba04e1121891\"],\"text\":\"Usage Policy\",\"_key\":\"5d488fbe87cd1\"},{\"marks\":[],\"text\":\" sets forth our standards for the use of our products, including rules that prohibit using our models to spread misinformation, incite violence or hateful behavior, or engage in fraudulent or abusive practices. We continually refine our technical measures for enforcing our trust and safety standards at scale. Further, we conduct research to understand the broader \",\"_key\":\"5d488fbe87cd2\",\"_type\":\"span\"},{\"text\":\"societal impacts\",\"_key\":\"5d488fbe87cd3\",\"_type\":\"span\",\"marks\":[\"32798753cd99\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\" of our models. Our Responsible Scaling Policy complements our work in these areas, contributing to our understanding of current and potential risks.\",\"_key\":\"5d488fbe87cd4\"}],\"_type\":\"block\"},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"3b55c0bceff9\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"A framework for proportional safeguards\",\"_key\":\"5525570b2a010\",\"_type\":\"span\"}]},{\"style\":\"normal\",\"_key\":\"ad1d58dbdd5b\",\"markDefs\":[{\"_key\":\"f8e6f3368061\",\"_type\":\"link\",\"href\":\"https://en.wikipedia.org/wiki/Biosafety_level\"}],\"children\":[{\"_key\":\"cdd8f6a2997b0\",\"_type\":\"span\",\"marks\":[],\"text\":\"As before, we maintain our core commitment: we will not train or deploy models unless we have implemented safety and security measures that keep risks below acceptable levels. Our RSP is based on the principle of proportional protection: safeguards that scale with potential risks. To do this, we use \"},{\"_key\":\"3517fb380020\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"AI Safety Level Standards (ASL Standards)\"},{\"text\":\", graduated sets of safety and security measures that become more stringent as model capabilities increase. Inspired by \",\"_key\":\"aede15153eee\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"f8e6f3368061\"],\"text\":\"Biosafety Levels,\",\"_key\":\"cdd8f6a2997b1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" these begin at ASL-1 for models that have very basic capabilities (for example, chess-playing bots) and progress through ASL-2, ASL-3, and so on.\",\"_key\":\"cdd8f6a2997b2\"}],\"_type\":\"block\"},{\"_key\":\"18e2a21a8d24\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In our updated policy, we have refined our methodology for assessing specific capabilities (and their associated risks) and implementing proportional safety and security measures. Our updated framework has two key components:\",\"_key\":\"94961b6308040\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"110d48e0214c\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Capability Thresholds:\",\"_key\":\"4fa1b51cfc900\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_key\":\"f90398ece8b9\",\"_type\":\"span\",\"marks\":[],\"text\":\" Specific AI abilities that, if reached, would require stronger safeguards than our current baseline.\"}],\"level\":1},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"132d0001c926\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_key\":\"d0053cd57ea60\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Required Safeguards: \"},{\"_key\":\"663a30c5e5b1\",\"_type\":\"span\",\"marks\":[],\"text\":\"The specific ASL Standards needed to mitigate risks once a Capability Threshold has been reached.\"}],\"level\":1},{\"_key\":\"36479c291519\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"At present, all of our models operate under ASL-2 Standards, which reflect current industry best practices. Our updated policy defines two key Capability Thresholds that would require upgraded safeguards:\",\"_key\":\"a03bfcd2c90a0\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Autonomous AI Research and Development:\",\"_key\":\"113a03232f950\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can independently conduct complex AI research tasks typically requiring human expertise—potentially significantly accelerating AI development in an unpredictable way—we require elevated security standards (potentially ASL-4 or higher standards) and additional safety assurances to avoid a situation where development outpaces our ability to address emerging risks.\",\"_key\":\"559bae44e291\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7960a2e610b8\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b1e7e0200f28\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Chemical, Biological, Radiological, and Nuclear (CBRN) weapons:\",\"_key\":\"04f8008234340\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can meaningfully assist someone with a basic technical background in creating or deploying CBRN weapons, we require enhanced security and deployment safeguards (ASL-3 standards).\",\"_key\":\"e87c145d832e\"}],\"level\":1},{\"_key\":\"5028d8139378\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"ASL-3 safeguards involve enhanced security measures and deployment controls. On the security side, this will include internal access controls and more robust protection of model weights. For deployment risks, we plan to implement a multi-layered approach to prevent misuse, including real-time and asynchronous monitoring, rapid response protocols, and thorough pre-deployment red teaming.\",\"_key\":\"c21521b5c8660\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Implementation and oversight\",\"_key\":\"c9476c97d6320\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"9d149ea1669f\",\"markDefs\":[]},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"To contribute to effective implementation of the policy, we have established:\",\"_key\":\"0898891e3fd00\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"081edde580ae\",\"markDefs\":[]},{\"children\":[{\"text\":\"Capability assessments\",\"_key\":\"65ad635ebc020\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\": Routine model evaluations based on our Capability Thresholds to determine whether our current safeguards are still appropriate. (Summaries of past assessments are available \",\"_key\":\"ee8bb23f5481\"},{\"_type\":\"span\",\"marks\":[\"e97965203d94\"],\"text\":\"here\",\"_key\":\"65ad635ebc021\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".)\",\"_key\":\"65ad635ebc022\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"1ef822ef2fa2\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"e97965203d94\"}]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"d6b74c71ad6d\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Safeguard assessments: \",\"_key\":\"fd96692cf5950\"},{\"marks\":[],\"text\":\"Routine evaluation of the effectiveness of our security and deployment safety measures to assess whether we have met the Required Safeguards bar. (Summaries of these decisions will be available \",\"_key\":\"bbdf4a0ded13\",\"_type\":\"span\"},{\"text\":\"here\",\"_key\":\"fd96692cf5951\",\"_type\":\"span\",\"marks\":[\"d6b74c71ad6d\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\".)\",\"_key\":\"fd96692cf5952\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b5fc19b935bb\",\"listItem\":\"bullet\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"8074ca752c13\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2403.10462\",\"_key\":\"dd97b493d101\"}],\"children\":[{\"text\":\"Documentation and decision-making: \",\"_key\":\"ed8d6ec6368a0\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_key\":\"d451a32ef6cb\",\"_type\":\"span\",\"marks\":[],\"text\":\"Processes for documenting the capability and safeguard assessments, inspired by procedures (such as \"},{\"_type\":\"span\",\"marks\":[\"dd97b493d101\"],\"text\":\"safety case methodologies\",\"_key\":\"ed8d6ec6368a1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\") common in high-reliability industries.\",\"_key\":\"ed8d6ec6368a2\"}],\"level\":1},{\"markDefs\":[],\"children\":[{\"marks\":[\"strong\"],\"text\":\"Measures for internal governance and external input: \",\"_key\":\"ca95d473f4a90\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Our assessment methodology will be backed up by internal stress-testing in addition to our existing internal reporting process for safety issues. We are also soliciting external expert feedback on our methodologies.\",\"_key\":\"fb68635efd29\"},{\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"1\",\"_key\":\"7ddd60e07d17\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"60c9b7dabd40\",\"listItem\":\"bullet\"},{\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Learning from experience\",\"_key\":\"ecdac5e44ed40\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"6e5effe0bac6\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"We have learned a lot in our first year with the previous RSP in effect, and are using this update as an opportunity to reflect on what has worked well and what makes sense to update in the policy. As part of this, we conducted our first review of how well we adhered to the framework and identified a small number of instances where we fell short of meeting the full letter of its requirements. These included procedural issues such as completing a set of evaluations three days later than scheduled or a lack of clarity on how and where we should note any changes to our placeholder evaluations. We also flagged some evaluations where we may have been able to elicit slightly better model performance through implementing standard techniques (such as chain-of-thought or best-of-N).\",\"_key\":\"8932eeee77830\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"142037ff4afb\",\"markDefs\":[]},{\"style\":\"normal\",\"_key\":\"fdd39c742e14\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://anthropic.com/rsp-updates\",\"_key\":\"724c1bcc37c6\"}],\"children\":[{\"marks\":[],\"text\":\"In all cases, we found these instances posed minimal risk to the safety of our models. We used the additional three days to refine and improve our evaluations; the different set of evaluations we used provided a more accurate assessment than the placeholder evaluations; and our evaluation methodology still showed we were sufficiently far from the thresholds. From this, we learned two valuable lessons to incorporate into our updated framework: we needed to incorporate more flexibility into our policies, and we needed to improve our process for tracking compliance with the RSP. You can read more \",\"_key\":\"2c9de8a77cf10\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"724c1bcc37c6\"],\"text\":\"here\",\"_key\":\"2c9de8a77cf11\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\",\"_key\":\"2c9de8a77cf12\"}],\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"c84e70ee115b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Since we first released the RSP a year ago, our goal has been to offer an example of a framework that others might draw inspiration from when crafting their own AI risk governance policies. We hope that proactively sharing our experiences implementing our own policy will help other companies in implementing their own risk management frameworks and contribute to the establishment of best practices across the AI ecosystem.\",\"_key\":\"855d60a456920\"}],\"_type\":\"block\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Looking ahead\",\"_key\":\"81a081f760a10\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"20a9139b3a51\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The frontier of AI is advancing rapidly, making it challenging to anticipate what safety measures will be appropriate for future systems. All aspects of our safety program will continue to evolve: our policies, evaluation methodology, safeguards, and our research into potential risks and mitigations.\",\"_key\":\"252e5f0060c50\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"78baf5750bd0\"},{\"_key\":\"2afadf824368\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Additionally, Co-Founder and Chief Science Officer Jared Kaplan will serve as Anthropic’s Responsible Scaling Officer, succeeding Co-Founder and Chief Technology Officer Sam McCandlish who held this role over the last year. Sam oversaw the RSP’s initial implementation and will continue to focus on his duties as Chief Technology Officer. As we work to scale up our efforts on implementing the RSP, we’re also opening a position for a Head of Responsible Scaling. This role will be responsible for coordinating the many teams needed to iterate on and successfully comply with the RSP.\",\"_key\":\"744b6d45ddff0\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"88e4ecab085f\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/jobs\",\"_key\":\"6210958b799c\"}],\"children\":[{\"text\":\"If you would like to contribute to AI risk management at Anthropic, \",\"_key\":\"d603f352b2580\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"6210958b799c\"],\"text\":\"we are hiring\",\"_key\":\"d0297560aab0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"! Many of our teams now contribute to risk management via the RSP, including:\",\"_key\":\"ec2a1674bc0a\"}]},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"803749c5db22\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Frontier Red Team (responsible for threat modeling and capability assessments)\",\"_key\":\"0a4d9f27a0cd0\",\"_type\":\"span\",\"marks\":[]}],\"level\":1},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"fe760029da3a\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Trust \u0026 Safety (responsible for developing deployment safeguards)\",\"_key\":\"dc5557c893ca0\"}],\"level\":1},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Security and Compliance (responsible for security safeguards and risk 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Our model card shows comparisons to models that have been announced but not yet released, such as Gemini 1.5 Pro. In addition, we’d like to note that engineers have worked to optimize prompts and few-shot samples for evaluations and reported higher scores for a newer GPT-4T model. \",\"_key\":\"137b54da87990\"},{\"_key\":\"b4ba8cc2e443\",\"_type\":\"span\",\"marks\":[\"b3d47055ad85\"],\"text\":\"Source\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\",\"_key\":\"04f51fedd06a\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"95b520db64f4\",\"listItem\":\"number\"}],\"_rev\":\"XBP1YM482O189X05gwOKSe\",\"body\":[{\"markDefs\":[],\"children\":[{\"_key\":\"e3f6ee680e7d0\",\"_type\":\"span\",\"marks\":[],\"text\":\"\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"e9ca761c9e34\"},{\"style\":\"inline\",\"_key\":\"8fe584460916\",\"asset\":{\"_ref\":\"image-4e78f69ef8d4186fb5691714abe36224483d91b0-2880x1620-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"description\":\"Claude 3 \"},{\"markDefs\":[{\"_key\":\"e690c5685704\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/api#pricing\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Today, we're announcing the Claude 3 model family, which sets new industry benchmarks across a wide range of cognitive tasks. The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and \",\"_key\":\"6c7c68493bd10\"},{\"_type\":\"span\",\"marks\":[\"e690c5685704\"],\"text\":\"cost\",\"_key\":\"1309100e4264\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" for their specific application.\",\"_key\":\"443337375366\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"d4714c0f2d48\"},{\"style\":\"normal\",\"_key\":\"496fb9c9f406\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/supported-countries\",\"_key\":\"b2d9865719c6\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Opus and Sonnet are now available to use in claude.ai and the Claude API which is now generally available in \",\"_key\":\"b0e67afac1800\"},{\"marks\":[\"b2d9865719c6\"],\"text\":\"159 countries\",\"_key\":\"cfd556a17bfc\",\"_type\":\"span\"},{\"_key\":\"c2f391fd5235\",\"_type\":\"span\",\"marks\":[],\"text\":\". Haiku will be available soon.\"}],\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"a530c9ea6fe9\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"\",\"_key\":\"799454141bb4\",\"_type\":\"span\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Claude 3 model family\",\"_key\":\"59528945df81\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"2889706d30e0\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"2f4d381f922a\",\"asset\":{\"_ref\":\"image-5d20371eeb8d045465bb22cacfd269b5958b004d-2200x1174-png\",\"_type\":\"reference\"}},{\"style\":\"h3\",\"_key\":\"34dbfdf7ee0b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"A new standard for intelligence\",\"_key\":\"7c88ca38cc48\"}],\"_type\":\"block\"},{\"markDefs\":[],\"children\":[{\"text\":\"Opus, our most intelligent model, outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.\",\"_key\":\"b2c1b6abdeda0\",\"_type\":\"span\",\"marks\":[]}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"6d478b0f5b1a\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"791ebb9539f9\",\"markDefs\":[{\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"aca6e529fda4\",\"_type\":\"link\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"All \",\"_key\":\"ea2185156be50\"},{\"marks\":[\"aca6e529fda4\"],\"text\":\"Claude 3\",\"_key\":\"f41e11b72a5a\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" models show increased capabilities in analysis and forecasting, nuanced content creation, code generation, and conversing in non-English languages like Spanish, Japanese, and French.\",\"_key\":\"ce9cf674fb4f\"}]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Below is a comparison of the Claude 3 models to those of our peers on multiple benchmarks [1] of capability:\",\"_key\":\"e66d47cf953f0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b77f3b42f058\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"96b31e47a780\",\"asset\":{\"_ref\":\"image-9ad98d612086fe52b3042f9183414669b4d2a3da-2200x1954-png\",\"_type\":\"reference\"}},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"122d99955c94\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Near-instant results\",\"_key\":\"94ebd918295e0\"}]},{\"_key\":\"a716760d26f9\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 models can power live customer chats, auto-completions, and data extraction tasks where responses must be immediate and in real-time.\",\"_key\":\"d636443bda240\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Haiku is the fastest and most cost-effective model on the market for its intelligence category. It can read an information and data dense research paper on arXiv (~10k tokens) with charts and graphs in less than three seconds. Following launch, we expect to improve performance even further.\",\"_key\":\"c30fe08114e00\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"ef96ccac31b1\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"2c59a54bb9cf\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 with higher levels of intelligence. It excels at tasks demanding rapid responses, like knowledge retrieval or sales automation. Opus delivers similar speeds to Claude 2 and 2.1, but with much higher levels of intelligence.\",\"_key\":\"5d256ef7b4150\"}]},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"118680357dd1\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Strong vision capabilities\",\"_key\":\"e67b4b4e5440\",\"_type\":\"span\"}]},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 models have sophisticated vision capabilities on par with other leading models. They can process a wide range of visual formats, including photos, charts, graphs and technical diagrams. We’re particularly excited to provide this new modality to our enterprise customers, some of whom have up to 50% of their knowledge bases encoded in various formats such as PDFs, flowcharts, or presentation slides.\",\"_key\":\"69e310a95d7b0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"4c7c669b3031\",\"markDefs\":[]},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"5b0b439445a2\",\"asset\":{\"_ref\":\"image-6b66d86ff0c180e95bc6ad2e6e4a1843aa74c80f-2200x960-png\",\"_type\":\"reference\"}},{\"children\":[{\"marks\":[],\"text\":\"Fewer refusals\",\"_key\":\"102fdd5307960\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"e1adb462b46f\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Previous Claude models often made unnecessary refusals that suggested a lack of contextual understanding. We’ve made meaningful progress in this area: Opus, Sonnet, and Haiku are significantly less likely to refuse to answer prompts that border on the system’s guardrails than previous generations of models. As shown below, the Claude 3 models show a more nuanced understanding of requests, recognize real harm, and refuse to answer harmless prompts much less often.\",\"_key\":\"bf8b70935bb90\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"19ea17e89d91\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"a78c6685664b\",\"asset\":{\"_ref\":\"image-d1fbcf3d58ebc2dcd2e98aac995d70bf50cb2e9c-2188x918-png\",\"_type\":\"reference\"}},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"2e52cdedf221\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Improved accuracy\",\"_key\":\"7f14def3c85c\"}]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Businesses of all sizes rely on our models to serve their customers, making it imperative for our model outputs to maintain high accuracy at scale. To assess this, we use a large set of complex, factual questions that target known weaknesses in current models. We categorize the responses into correct answers, incorrect answers (or hallucinations), and admissions of uncertainty, where the model says it doesn’t know the answer instead of providing incorrect information. Compared to Claude 2.1, Opus demonstrates a twofold improvement in accuracy (or correct answers) on these challenging open-ended questions while also exhibiting reduced levels of incorrect answers.\",\"_key\":\"692654be5f0a0\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"17889c1cfe91\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"0d0ef8e29c15\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In addition to producing more trustworthy responses, we will soon enable citations in our Claude 3 models so they can point to precise sentences in reference material to verify their answers.\\n\",\"_key\":\"dabf8032cfab0\"}]},{\"asset\":{\"_ref\":\"image-7cb598c6a9fa58c12b77f67ee2067feaac4a2de0-2200x896-png\",\"_type\":\"reference\"},\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"06407e249d10\"},{\"markDefs\":[],\"children\":[{\"_key\":\"5db36703ea090\",\"_type\":\"span\",\"marks\":[],\"text\":\"Long context and near-perfect recall\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"fcfee2b49205\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"48daeea9bc01\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The Claude 3 family of models will initially offer a 200K context window upon launch. However, all three models are capable of accepting inputs exceeding 1 million tokens and we may make this available to select customers who need enhanced processing power.\",\"_key\":\"69e1354a626c0\"}]},{\"children\":[{\"marks\":[],\"text\":\"To process long context prompts effectively, models require robust recall capabilities. The 'Needle In A Haystack' (NIAH) evaluation measures a model's ability to accurately recall information from a vast corpus of data. We enhanced the robustness of this benchmark by using one of 30 random needle/question pairs per prompt and testing on a diverse crowdsourced corpus of documents. Claude 3 Opus not only achieved near-perfect recall, surpassing 99% accuracy, but in some cases, it even identified the limitations of the evaluation itself by recognizing that the \\\"needle\\\" sentence appeared to be artificially inserted into the original text by a human.\",\"_key\":\"bde3af4ac3670\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7dc25016e60c\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"\",\"_key\":\"f4679e3b7a80\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"0015926d72c6\"},{\"_type\":\"image\",\"style\":\"inline\",\"_key\":\"91eb42b8ae41\",\"asset\":{\"_ref\":\"image-d2aa12b60e9c57e7057924bd8878d754c7b3d8e7-2200x1088-png\",\"_type\":\"reference\"}},{\"style\":\"h3\",\"_key\":\"aa397b8752f3\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Responsible design\",\"_key\":\"dd43651c5b1f0\"}],\"_type\":\"block\"},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/news/constitutional-ai-harmlessness-from-ai-feedback\",\"_key\":\"b608daa539d5\"}],\"children\":[{\"marks\":[],\"text\":\"We’ve developed the Claude 3 family of models to be as trustworthy as they are capable. We have several dedicated teams that track and mitigate a broad spectrum of risks, ranging from misinformation and CSAM to biological misuse, election interference, and autonomous replication skills. We continue to develop methods such as \",\"_key\":\"aa543e64358e0\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"b608daa539d5\"],\"text\":\"Constitutional AI\",\"_key\":\"a30dab63a714\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" that improve the safety and transparency of our models, and have tuned our models to mitigate against privacy issues that could be raised by new modalities.\",\"_key\":\"5b0d68dcb1cf\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9c58cc4307c0\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"538d63deb7c9\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://aclanthology.org/2022.findings-acl.165/\",\"_key\":\"8b8565815b0e\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Addressing biases in increasingly sophisticated models is an ongoing effort and we’ve made strides with this new release. As shown in the model card, Claude 3 shows less biases than our previous models according to the \",\"_key\":\"e6f420b3c2a00\"},{\"_type\":\"span\",\"marks\":[\"8b8565815b0e\"],\"text\":\"Bias Benchmark for Question Answering (BBQ)\",\"_key\":\"9b0d6e529f30\"},{\"marks\":[],\"text\":\". We remain committed to advancing techniques that reduce biases and promote greater neutrality in our models, ensuring they are not skewed towards any particular partisan stance.\",\"_key\":\"838b9d7eaaf5\",\"_type\":\"span\"}]},{\"markDefs\":[{\"href\":\"https://www.anthropic.com/news/anthropics-responsible-scaling-policy\",\"_key\":\"a6acdb8c6c9a\",\"_type\":\"link\"},{\"href\":\"https://www.anthropic.com/news/red-teaming-language-models-to-reduce-harms-methods-scaling-behaviors-and-lessons-learned\",\"_key\":\"41ce6e74280d\",\"_type\":\"link\"},{\"_type\":\"link\",\"href\":\"https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/\",\"_key\":\"bff6073a96fa\"},{\"_type\":\"link\",\"href\":\"https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/\",\"_key\":\"34ede29139ed\"},{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/claude-3-model-card\",\"_key\":\"149416bc7eda\"}],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"While the Claude 3 model family has advanced on key measures of biological knowledge, cyber-related knowledge, and autonomy compared to previous models, it remains at AI Safety Level 2 (ASL-2) per our \",\"_key\":\"758320609d91\"},{\"_type\":\"span\",\"marks\":[\"a6acdb8c6c9a\"],\"text\":\"Responsible Scaling Policy\",\"_key\":\"2c7eb5dc236c\"},{\"text\":\". Our \",\"_key\":\"10f95acda51c\",\"_type\":\"span\",\"marks\":[]},{\"_key\":\"ba6380a89fd3\",\"_type\":\"span\",\"marks\":[\"41ce6e74280d\"],\"text\":\"red teaming\"},{\"text\":\" evaluations (performed in line with our \",\"_key\":\"db279d3786f3\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"bff6073a96fa\"],\"text\":\"White House commitments\",\"_key\":\"1941ee0371ec\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" and the \",\"_key\":\"c1d1c30839be\"},{\"_type\":\"span\",\"marks\":[\"34ede29139ed\"],\"text\":\"2023 US Executive Order\",\"_key\":\"bef6d5b627ce\"},{\"text\":\") have concluded that the models present negligible potential for catastrophic risk at this time. We will continue to carefully monitor future models to assess their proximity to the ASL-3 threshold. Further safety details are available in the \",\"_key\":\"0f45812a053e\",\"_type\":\"span\",\"marks\":[]},{\"text\":\"Claude 3 model card\",\"_key\":\"cfb31e9a0274\",\"_type\":\"span\",\"marks\":[\"149416bc7eda\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\".\\n\",\"_key\":\"f716b43bd491\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"27e6c2393187\"},{\"style\":\"h3\",\"_key\":\"51f637ad41d6\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Easier to use\",\"_key\":\"50d56ac801a9\",\"_type\":\"span\"}],\"_type\":\"block\"},{\"children\":[{\"marks\":[],\"text\":\"The Claude 3 models are better at following complex, multi-step instructions. They are particularly adept at adhering to brand voice and response guidelines, and developing customer-facing experiences our users can trust. In addition, the Claude 3 models are better at producing popular structured output in formats like JSON—making it simpler to instruct Claude for use cases like natural language classification and sentiment analysis.\\n\",\"_key\":\"f53f06fc023c0\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"9eaf0570bbc3\",\"markDefs\":[]},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"22de8c15e566\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"\",\"_key\":\"6d09ab0857020\"}]},{\"style\":\"h3\",\"_key\":\"f0ecb4920f1b\",\"markDefs\":[],\"children\":[{\"_key\":\"712d5a95b1a60\",\"_type\":\"span\",\"marks\":[],\"text\":\"Model details\"}],\"_type\":\"block\"},{\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Claude 3 Opus \",\"_key\":\"fc4095508509\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"is our most intelligent model, with best-in-market performance on highly complex tasks. It can navigate open-ended prompts and sight-unseen scenarios with remarkable fluency and human-like understanding. 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After a year of implementation and learning, we are now sharing a significantly updated version that reflects practical insights and accounts for advancing technological capabilities.\",\"_key\":\"b75aa93fa2c82\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"c8b393e2960c\"},{\"style\":\"normal\",\"_key\":\"a1948e4cc873\",\"markDefs\":[{\"_key\":\"ba04e1121891\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/legal/aup\"},{\"_key\":\"32798753cd99\",\"_type\":\"link\",\"href\":\"https://www.anthropic.com/research#societal-impacts\"}],\"children\":[{\"marks\":[],\"text\":\"Although this policy focuses on catastrophic risks like the categories listed below, they are not the only risks that we monitor and prepare for. Our \",\"_key\":\"5d488fbe87cd0\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"ba04e1121891\"],\"text\":\"Usage Policy\",\"_key\":\"5d488fbe87cd1\"},{\"marks\":[],\"text\":\" sets forth our standards for the use of our products, including rules that prohibit using our models to spread misinformation, incite violence or hateful behavior, or engage in fraudulent or abusive practices. We continually refine our technical measures for enforcing our trust and safety standards at scale. Further, we conduct research to understand the broader \",\"_key\":\"5d488fbe87cd2\",\"_type\":\"span\"},{\"text\":\"societal impacts\",\"_key\":\"5d488fbe87cd3\",\"_type\":\"span\",\"marks\":[\"32798753cd99\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\" of our models. Our Responsible Scaling Policy complements our work in these areas, contributing to our understanding of current and potential risks.\",\"_key\":\"5d488fbe87cd4\"}],\"_type\":\"block\"},{\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"3b55c0bceff9\",\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"A framework for proportional safeguards\",\"_key\":\"5525570b2a010\",\"_type\":\"span\"}]},{\"style\":\"normal\",\"_key\":\"ad1d58dbdd5b\",\"markDefs\":[{\"_key\":\"f8e6f3368061\",\"_type\":\"link\",\"href\":\"https://en.wikipedia.org/wiki/Biosafety_level\"}],\"children\":[{\"_key\":\"cdd8f6a2997b0\",\"_type\":\"span\",\"marks\":[],\"text\":\"As before, we maintain our core commitment: we will not train or deploy models unless we have implemented safety and security measures that keep risks below acceptable levels. Our RSP is based on the principle of proportional protection: safeguards that scale with potential risks. To do this, we use \"},{\"_key\":\"3517fb380020\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"AI Safety Level Standards (ASL Standards)\"},{\"text\":\", graduated sets of safety and security measures that become more stringent as model capabilities increase. Inspired by \",\"_key\":\"aede15153eee\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"f8e6f3368061\"],\"text\":\"Biosafety Levels,\",\"_key\":\"cdd8f6a2997b1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" these begin at ASL-1 for models that have very basic capabilities (for example, chess-playing bots) and progress through ASL-2, ASL-3, and so on.\",\"_key\":\"cdd8f6a2997b2\"}],\"_type\":\"block\"},{\"_key\":\"18e2a21a8d24\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"In our updated policy, we have refined our methodology for assessing specific capabilities (and their associated risks) and implementing proportional safety and security measures. Our updated framework has two key components:\",\"_key\":\"94961b6308040\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"110d48e0214c\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Capability Thresholds:\",\"_key\":\"4fa1b51cfc900\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_key\":\"f90398ece8b9\",\"_type\":\"span\",\"marks\":[],\"text\":\" Specific AI abilities that, if reached, would require stronger safeguards than our current baseline.\"}],\"level\":1},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"132d0001c926\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_key\":\"d0053cd57ea60\",\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Required Safeguards: \"},{\"_key\":\"663a30c5e5b1\",\"_type\":\"span\",\"marks\":[],\"text\":\"The specific ASL Standards needed to mitigate risks once a Capability Threshold has been reached.\"}],\"level\":1},{\"_key\":\"36479c291519\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"At present, all of our models operate under ASL-2 Standards, which reflect current industry best practices. Our updated policy defines two key Capability Thresholds that would require upgraded safeguards:\",\"_key\":\"a03bfcd2c90a0\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Autonomous AI Research and Development:\",\"_key\":\"113a03232f950\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can independently conduct complex AI research tasks typically requiring human expertise—potentially significantly accelerating AI development in an unpredictable way—we require elevated security standards (potentially ASL-4 or higher standards) and additional safety assurances to avoid a situation where development outpaces our ability to address emerging risks.\",\"_key\":\"559bae44e291\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"7960a2e610b8\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b1e7e0200f28\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Chemical, Biological, Radiological, and Nuclear (CBRN) weapons:\",\"_key\":\"04f8008234340\"},{\"_type\":\"span\",\"marks\":[],\"text\":\" If a model can meaningfully assist someone with a basic technical background in creating or deploying CBRN weapons, we require enhanced security and deployment safeguards (ASL-3 standards).\",\"_key\":\"e87c145d832e\"}],\"level\":1},{\"_key\":\"5028d8139378\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"ASL-3 safeguards involve enhanced security measures and deployment controls. On the security side, this will include internal access controls and more robust protection of model weights. For deployment risks, we plan to implement a multi-layered approach to prevent misuse, including real-time and asynchronous monitoring, rapid response protocols, and thorough pre-deployment red teaming.\",\"_key\":\"c21521b5c8660\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Implementation and oversight\",\"_key\":\"c9476c97d6320\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"9d149ea1669f\",\"markDefs\":[]},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"To contribute to effective implementation of the policy, we have established:\",\"_key\":\"0898891e3fd00\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"081edde580ae\",\"markDefs\":[]},{\"children\":[{\"text\":\"Capability assessments\",\"_key\":\"65ad635ebc020\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\": Routine model evaluations based on our Capability Thresholds to determine whether our current safeguards are still appropriate. (Summaries of past assessments are available \",\"_key\":\"ee8bb23f5481\"},{\"_type\":\"span\",\"marks\":[\"e97965203d94\"],\"text\":\"here\",\"_key\":\"65ad635ebc021\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".)\",\"_key\":\"65ad635ebc022\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"1ef822ef2fa2\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"e97965203d94\"}]},{\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://www.anthropic.com/rsp-updates\",\"_key\":\"d6b74c71ad6d\"}],\"children\":[{\"_type\":\"span\",\"marks\":[\"strong\"],\"text\":\"Safeguard assessments: \",\"_key\":\"fd96692cf5950\"},{\"marks\":[],\"text\":\"Routine evaluation of the effectiveness of our security and deployment safety measures to assess whether we have met the Required Safeguards bar. (Summaries of these decisions will be available \",\"_key\":\"bbdf4a0ded13\",\"_type\":\"span\"},{\"text\":\"here\",\"_key\":\"fd96692cf5951\",\"_type\":\"span\",\"marks\":[\"d6b74c71ad6d\"]},{\"_type\":\"span\",\"marks\":[],\"text\":\".)\",\"_key\":\"fd96692cf5952\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"b5fc19b935bb\",\"listItem\":\"bullet\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"8074ca752c13\",\"listItem\":\"bullet\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://arxiv.org/abs/2403.10462\",\"_key\":\"dd97b493d101\"}],\"children\":[{\"text\":\"Documentation and decision-making: \",\"_key\":\"ed8d6ec6368a0\",\"_type\":\"span\",\"marks\":[\"strong\"]},{\"_key\":\"d451a32ef6cb\",\"_type\":\"span\",\"marks\":[],\"text\":\"Processes for documenting the capability and safeguard assessments, inspired by procedures (such as \"},{\"_type\":\"span\",\"marks\":[\"dd97b493d101\"],\"text\":\"safety case methodologies\",\"_key\":\"ed8d6ec6368a1\"},{\"_type\":\"span\",\"marks\":[],\"text\":\") common in high-reliability industries.\",\"_key\":\"ed8d6ec6368a2\"}],\"level\":1},{\"markDefs\":[],\"children\":[{\"marks\":[\"strong\"],\"text\":\"Measures for internal governance and external input: \",\"_key\":\"ca95d473f4a90\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"Our assessment methodology will be backed up by internal stress-testing in addition to our existing internal reporting process for safety issues. We are also soliciting external expert feedback on our methodologies.\",\"_key\":\"fb68635efd29\"},{\"_type\":\"span\",\"marks\":[\"sup\"],\"text\":\"1\",\"_key\":\"7ddd60e07d17\"}],\"level\":1,\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"60c9b7dabd40\",\"listItem\":\"bullet\"},{\"markDefs\":[],\"children\":[{\"marks\":[],\"text\":\"Learning from experience\",\"_key\":\"ecdac5e44ed40\",\"_type\":\"span\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"6e5effe0bac6\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"We have learned a lot in our first year with the previous RSP in effect, and are using this update as an opportunity to reflect on what has worked well and what makes sense to update in the policy. As part of this, we conducted our first review of how well we adhered to the framework and identified a small number of instances where we fell short of meeting the full letter of its requirements. These included procedural issues such as completing a set of evaluations three days later than scheduled or a lack of clarity on how and where we should note any changes to our placeholder evaluations. We also flagged some evaluations where we may have been able to elicit slightly better model performance through implementing standard techniques (such as chain-of-thought or best-of-N).\",\"_key\":\"8932eeee77830\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"142037ff4afb\",\"markDefs\":[]},{\"style\":\"normal\",\"_key\":\"fdd39c742e14\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"http://anthropic.com/rsp-updates\",\"_key\":\"724c1bcc37c6\"}],\"children\":[{\"marks\":[],\"text\":\"In all cases, we found these instances posed minimal risk to the safety of our models. We used the additional three days to refine and improve our evaluations; the different set of evaluations we used provided a more accurate assessment than the placeholder evaluations; and our evaluation methodology still showed we were sufficiently far from the thresholds. From this, we learned two valuable lessons to incorporate into our updated framework: we needed to incorporate more flexibility into our policies, and we needed to improve our process for tracking compliance with the RSP. You can read more \",\"_key\":\"2c9de8a77cf10\",\"_type\":\"span\"},{\"_type\":\"span\",\"marks\":[\"724c1bcc37c6\"],\"text\":\"here\",\"_key\":\"2c9de8a77cf11\"},{\"_type\":\"span\",\"marks\":[],\"text\":\".\",\"_key\":\"2c9de8a77cf12\"}],\"_type\":\"block\"},{\"style\":\"normal\",\"_key\":\"c84e70ee115b\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Since we first released the RSP a year ago, our goal has been to offer an example of a framework that others might draw inspiration from when crafting their own AI risk governance policies. We hope that proactively sharing our experiences implementing our own policy will help other companies in implementing their own risk management frameworks and contribute to the establishment of best practices across the AI ecosystem.\",\"_key\":\"855d60a456920\"}],\"_type\":\"block\"},{\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Looking ahead\",\"_key\":\"81a081f760a10\"}],\"_type\":\"block\",\"style\":\"h3\",\"_key\":\"20a9139b3a51\",\"markDefs\":[]},{\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"The frontier of AI is advancing rapidly, making it challenging to anticipate what safety measures will be appropriate for future systems. All aspects of our safety program will continue to evolve: our policies, evaluation methodology, safeguards, and our research into potential risks and mitigations.\",\"_key\":\"252e5f0060c50\"}],\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"78baf5750bd0\"},{\"_key\":\"2afadf824368\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Additionally, Co-Founder and Chief Science Officer Jared Kaplan will serve as Anthropic’s Responsible Scaling Officer, succeeding Co-Founder and Chief Technology Officer Sam McCandlish who held this role over the last year. Sam oversaw the RSP’s initial implementation and will continue to focus on his duties as Chief Technology Officer. As we work to scale up our efforts on implementing the RSP, we’re also opening a position for a Head of Responsible Scaling. This role will be responsible for coordinating the many teams needed to iterate on and successfully comply with the RSP.\",\"_key\":\"744b6d45ddff0\"}],\"_type\":\"block\",\"style\":\"normal\"},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"88e4ecab085f\",\"markDefs\":[{\"_type\":\"link\",\"href\":\"https://www.anthropic.com/jobs\",\"_key\":\"6210958b799c\"}],\"children\":[{\"text\":\"If you would like to contribute to AI risk management at Anthropic, \",\"_key\":\"d603f352b2580\",\"_type\":\"span\",\"marks\":[]},{\"_type\":\"span\",\"marks\":[\"6210958b799c\"],\"text\":\"we are hiring\",\"_key\":\"d0297560aab0\"},{\"_type\":\"span\",\"marks\":[],\"text\":\"! Many of our teams now contribute to risk management via the RSP, including:\",\"_key\":\"ec2a1674bc0a\"}]},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"803749c5db22\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"text\":\"Frontier Red Team (responsible for threat modeling and capability assessments)\",\"_key\":\"0a4d9f27a0cd0\",\"_type\":\"span\",\"marks\":[]}],\"level\":1},{\"_type\":\"block\",\"style\":\"normal\",\"_key\":\"fe760029da3a\",\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Trust \u0026 Safety (responsible for developing deployment safeguards)\",\"_key\":\"dc5557c893ca0\"}],\"level\":1},{\"listItem\":\"bullet\",\"markDefs\":[],\"children\":[{\"_type\":\"span\",\"marks\":[],\"text\":\"Security and Compliance (responsible for security safeguards and risk 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