今年早些时候，我们开展了一项试点项目，让外部研究人员能够访问聚合的、真实世界的 Claude 使用数据。三个研究团队利用 Anthropic Insights（我们的隐私保护分析工具）自行设计了研究方案；我们代表他们进行数据收集，而他们则开展独立的分析。在这篇文章中，我们分享了这些研究的高层结果，以及我们在运行这一试点项目中学到的经验。我们还为未来可能希望与我们合作的研究人员提供了一份意向表达表格。
确保向变革性 AI 的过渡顺利进行，需要理解其对人类和社会的影响。目前，关于 AI 真实世界交互的数据集中在少数几家实验室手中。我们认为，让更多数据广泛可用——面向研究人员、政策制定者和公众——将是一件好事。
实验室之外的研究人员有两个选择。他们可以利用实验室发布的分析结果，这些分析反映了真实使用情况，但往往回答的是实验室自身的问题，而非他们自己的问题。或者，他们可以使用公开数据集，这些数据可以按自己的意愿进行研究，但偏向于更随意的使用场景，可能无法反映大多数人实际使用 AI 的方式。对于就 AI 实际使用情况开展独立研究而言，这两种方式都不够充分。
今年春天，我们试点了一个项目，让三家外部研究机构通过 Anthropic Insights（原名为“Clio”）在 Claude 使用数据上设计并开展自己的研究。Anthropic Insights 是我们自家团队用来分析数百万次 Claude 对话使用模式的隐私保护工具。我们希望未来能扩大这一项目，因此我们还对所有与第三方研究人员共享的数据进行了额外的隐私审计，以验证我们的隐私保护措施依然有效（见附录）。
我们相信，这是外部研究人员首次基于 AI 公司自身的使用数据开展公开的独立研究。下面，我们将讨论外部团队发现了什么、我们在运行试点项目中学到了什么，以及我们在决定如何更广泛地扩展该项目时正在权衡的事项。我们还公开发布了每个项目的聚合数据。
研究人员发现了什么
我们与三个研究团队展开了合作：斯坦福大学的社会与语言技术（SALT）实验室、牛津大学的人类信息处理实验室，以及 METR——一家评估前沿 AI 模型的非营利组织。每个团队都自行制定了研究问题，并借助 Anthropic Insights 对 2026 年 4 月至 5 月期间约 25 万段 Claude.ai 或 Claude Code 对话进行了隐私保护分析。
我们希望外部合作伙伴尽可能保持独立，因此我们的合同审查权限仅限于用户隐私、可能帮助他人违反我们使用政策的信息、Anthropic 的机密信息以及研究准确性。除此之外，Anthropic 对研究结果的内容没有任何发言权，研究人员可以自由发表他们的成果，即使这些结果对 Anthropic 不利。以下是部分早期结果。我们对这些研究方向感到兴奋，也期待在数据公开后，其他人能发现更多有价值的内容。
社会与语言技术实验室研究了人类如何与 AI 协作。他们考察了人们会把哪些类型的工作交给 AI、人类在完成这些工作时保留了哪些角色，以及人机协作在哪些环节会出现问题。他们的发现如下：
- 人们将高利害工作交给 AI 的比例超出预期。此前的研究认为，人们大多会把低问责性任务委托给 AI，而把影响重大的任务（即影响他人或难以挽回的工作）留给自己。但 SALT 实验室发现，超过一半的 Claude 对话中，人们将影响重大的任务委托给了 AI。人们最倾向于在寻求专业指导时把这类工作交给 Claude，尤其是在法律或财务问题上。
- 在与 Claude 协作时，人们通常会主导并监督工作。在近四分之三的对话中，人们设定方向，Claude 提供协助，而且人们通常会调整其输出，而不是直接照搬。但即使在向 Claude 明确表达期望的输出时，人们对 Claude 所产出的内容的理解和吸收程度也各不相同。
- 人们在与 AI 协作时常常会遇到摩擦。然而，这种摩擦往往是有益的。人们花时间和精力去观察 Claude 如何尝试完成一项任务，找出提示词中哪里表述不清或被误解，并反复调整自己的指示方向——这些投入最终会带来更好的结果——它促使人们澄清自己的意图、打磨输出，或持续深入思考问题。
点击此处阅读他们的完整报告。
人类信息处理实验室正在研究人们在使用 Claude 时的感受，以及这种感受与 Claude 行为之间的关联。他们的初步结果表明：
- 人们在使用 AI 时的感受与 AI 的行为方式密切相关。研究人员发现，人类与 AI 的行为模式在对话中相伴出现：Claude 表现温暖时，人们往往更积极；Claude 拒绝或提出异议时，人们往往会反驳；Claude 表现古怪时，人们往往在智力上更加投入；而 Claude 单纯提供帮助时，人们往往显得满意。
- 人们在使用 AI 时的体验与在互联网其他地方的体验非常相似。研究人员发现，Claude 对话中沉浸感、挫败感和愉悦感等状态之间的模式，与另一项关于日常互联网浏览的研究结果高度相似，这表明人们与 AI 互动的方式与其他数字活动之间存在相似性。
他们仍在完成研究报告。报告公开后，我们会在此处添加链接。
METR 正在评估编程智能体带来的实际生产力提升，以及这些生产力增长如何随模型代际变化。他们对 Claude Code 对话的分析仍在进行中，但初步结果表明：
- 能力更强的模型可能为用户节省更多时间。METR 对比了 Claude 对任务在无 AI 情况下所需时长的估算，以及使用不同 Claude 模型时任务实际花费的时间。他们的初步发现表明，较新的模型相比旧模型带来了显著的加速效果。METR 计划随着分析的深入分享更多内容。
- AI 能够相当准确地估算任务所需时间。由于该分析依赖于 Claude 判断一项任务需要多长时间，METR 将这些判断与此前一项开发者研究中的已知完成时间进行了对比。Claude 的估算与实际开发者所用时间具有相关性。
- 下一步：衡量 AI 在多大程度上加速研究。METR 正在继续探究其研究如何揭示 AI 对研究人员工作提速的程度，随着 AI 越来越多地承担自身开发工作，这一点可能变得愈发重要。
他们仍在完成研究报告。待其公开发布后，我们会在本文添加相关链接。
我们团队的收获
在 AI 领域，共享使用数据在很大程度上是前所未有的，因此这项试点既是对此类项目运作方式的一次实验，也是一种以隐私保护方式支持第三方研究的途径。保护用户隐私和研究人员的独立性都至关重要，而这两点我们都做到了。Anthropic Insights 正是为此而设计——研究人员从未接触原始对话，只接触经过与我们内部工作相同的法律和隐私审查后的聚合输出。然而，所有这些都使得该试点相对于 AI 实验室通常的研究速度而言较为缓慢，且运行起来资源密集。这两个因素都对有效扩展该项目构成了挑战。关于我们如何运行这项试点的更多细节，请参阅附录。下文我们将讨论我们的收获以及如何应对出现的挑战。
从不同角度探讨同一问题是有价值的。我们部分合作伙伴的研究问题与内部正在推进的工作有所重叠。例如，METR 的提案与我们关于“智能体编码与专业技能的持续回报”的经济学研究类似。我们发现这种重叠很有价值：它让外部研究人员有机会审视类似数据并得出自己的结论。这些结论是否与我们的一致，我们将在他们的研究持续推进中持续关注。我们还让 METR 与我们的经济学团队建立了联系，并发现这种联系改善了两个研究团队的工作。
内部有效的研究方法需要调整才能适用于外部合作伙伴。在使用 Anthropic Insights 时，研究人员会写出诸如“此人正在寻求何种类型的指导？”这样的问题，Claude 会针对研究中的每一段对话给出回答。随后，这些回答会被汇总归类；研究人员只能看到最终的类别以及每个类别所占的对话百分比。由于我们依赖 Claude 的判断，该工具对问题的措辞十分敏感；措辞不当的问题可能会将对话归入不准确的类别。由于没有人能阅读底层对话，这些错误很难被发现。
在内部，我们通过在数周内反复迭代问题来管理这一风险。外部合作伙伴无法做到这一点，因为在共享每个数据集之前进行重复的隐私审查会使研究无法进行。取而代之的是，我们让合作伙伴在 WildChat（一个公开的人机对话数据集）上测试他们的问题，这样他们就可以将答案与底层对话进行核对。但 WildChat 的数据偏向休闲和创意用途，与 Claude 的流量特征不同，因此一些在 WildChat 上表现良好的问题，一旦应用到真实的 Claude 对话中，就会产生误导性的分类。我们通过提供关于如何解读 Anthropic Insights 输出的指导（见附录）来解决这一问题。展望未来，我们正在探索如何让外部研究人员更有效地提前设计他们的问题和类别。
在防范滥用的同时保持透明度。我们合作伙伴的 Anthropic Insights 输出中，某些类别涉及违反我们《可接受使用政策》或《服务条款》的行为——例如，有一类人群在寻求关于被禁止活动的指导。我们认为公众应当了解我们平台被滥用的情形，因此我们分享了大部分此类违规行为。例外情况是那些描述用户如何绕过我们安全防护措施（而非其试图实施的行为）的类别。每项研究中，受影响的类别和对话占比均低于 5%，并且在每种情况下，我们都告知了研究人员我们修改或删除了哪些聚类及其原因。作为标准做法，当 Anthropic Insights 发现此类违规行为时，我们会将汇总数据提交给我们的安全团队进行审查。这也是我们未来与外部研究人员合作的重要流程。
展望未来
理解 AI 对社会的影响，单靠 AI 公司自身是难以胜任的。真正的监督需要外部研究人员就真实世界的使用数据提出他们自己的问题，并独立发表他们的发现。
这项试点是一次实验：外部研究人员能否在不损害我们用户隐私的前提下，在我们的平台上开展独立研究？这项工作比我们预想的更具挑战性，我们也学到了很多经验教训，但到目前为止，答案似乎是肯定的。我们的合作伙伴开展了我们原本不会想到要自行设计的研究，并且每项研究都让我们对 AI 在现实世界中的影响有了新的认识。这是充满希望的第一步，但还有更多工作要做。
下一步，我们需要确定能否扩大这一项目的规模，既包括在上述约束条件下我们能支持哪些类型的研究，也包括我们能同时开展多少项研究。我们正在稳步推进，以确保隐私、安全和研究质量。我们希望了解研究人员的兴趣所在，以及他们想要研究什么课题。如果您是研究人员，并且访问 Anthropic Insights 能让您开展目前无法进行的工作，请填写此表格。
附录
完整附录可在此处查阅。其中介绍了我们开展该项目的具体方式，包括我们如何遴选三家合作伙伴、为解释项目目标及 Anthropic Insights 功能而撰写的研究入门指南，以及每个项目如何从提案推进到研究设计再到分析。附录还包含我们合作协议的细节，其中明确规定合作伙伴可以自由发布研究结果，即使这些结果对 Anthropic 不利。此外，我们还介绍了由伦敦帝国理工学院开展的第三方隐私审计，以及我们对所有已发布数据所遵循的隐私威胁模型。我们还提供了关于如何解读合作伙伴 Anthropic Insights 研究中所发布数据的指南。
贡献与致谢
Kunal Handa 主导了该项目，与合作伙伴共同完善其研究提案，起草了研究指南和合同，运行了合作伙伴的 Anthropic Insights 研究，帮助搭建了支持合作伙伴研究的技术基础设施，参与了 Anthropic Insights 成果的内部审查，并撰写了博客文章。Miranda Zhang 协调了合作伙伴关系和沟通工作，并为项目的所有部分做出了贡献。Gabriel Nicholas 协调了第三方隐私审计、Anthropic Insights 成果的内部审查，并为项目的所有部分做出了贡献。Miles McCain 撰写了关于解读 Anthropic Insights 成果的指南，开发了支持合作伙伴研究的技术基础设施，参与了 Anthropic Insights 成果的内部审查，并对博客文章和隐私威胁模型提供了反馈。Ryan Heller 为支持合作伙伴的研究贡献了技术基础设施。Saffron Huang 参与了 Anthropic Insights 成果的内部审查，并提供了关键反馈和讨论。Thomas Millar 和 Suzanne Wang 为支持合作伙伴的研究以及 Anthropic Insights 成果的内部审查贡献了技术基础设施。Shan Carter、Mo Julapalli、Matt Kearney、Sarah Pollack 和 Judy Shen 参与了 Anthropic Insights 成果的内部审查。Matthew Jagielski 为隐私威胁模型做出了贡献。Shaoyi Zhang 为支持合作伙伴的研究贡献了技术基础设施。Heather Whitney、Ankur Rathi、Aisling Keenan 和 David Saunders 在整个项目过程中提供了法律和隐私方面的指导。Jake Eaton 和 Sylvie Carr 为博客文章的框架构建和撰写做出了贡献。Jack Clark 和 Michael Stern 在整个过程中提供了宝贵的指导、支持和讨论。Deep Ganguli 在项目的所有阶段提供了详细的指导、组织支持和反馈。
此外，我们感谢 Miriam Chaum、Ishita Dasgupta、Esin Durmus、Adam Farina、Zoe Hitzig、Jerry Hong、Devin Kuokka、Hendson Lin、Maxim Massenkoff、Peter McCrory、Maryam Quasto、Nitarshan Rajkumar、Amie Rotherham、Divya Siddarth、Taylor Sorensen、Jerome Swannack、Alex Tamkin、Molly Villagra、Scott White 和 Charles Yang 提供的宝贵想法、讨论、反馈与支持。
感谢本项目中的合作伙伴：斯坦福大学社会与语言技术实验室的 Vishakh Padmakumar、Yijia Shao、Jennifer Wang、Diyi Yang 和 Dora Zhao，牛津大学人类信息处理实验室的 Tsvetomira Dumbalska、Hannah Rose Kirk 和 Christopher Summerfield，以及 METR 的 Joel Becker（现任职于 Anthropic）、Daniel Paleka 和 Parker Whitfill。
感谢执行第三方隐私审计的 Zexi Yao、Bozhidar Stevanoski、Peter Romov、Euodia Dodd、Xiaoxue Yang 和 Nataša Krčo。
引用
@online{handa2026enablingindependentresearch,
author = {Kunal Handa and Miranda Zhang and Gabriel Nicholas and Miles McCain and Ryan Heller and Saffron Huang and Thomas Millar and Suzanne Wang and Shan Carter and Mo Julapalli and Matt Kearney and Sarah Pollack and Judy Shen and Matthew Jagielski and Shaoyi Zhang and Heather Whitney and Ankur Rathi and Aisling Keenan and David Saunders and Jake Eaton and Sylvie Carr and Jack Clark and Michael Stern and Deep Ganguli},
title = {Enabling independent research on how people use Claude},
date = {2026-08-26},
year = {2026},
url = {www.anthropic.com/research/enabling-independent-research},
}
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审视关于劳动者再培训项目的证据
Earlier this year, we ran a pilot giving external researchers access to aggregate, real-world Claude usage data. Three research groups designed their own studies for Anthropic Insights, our privacy-preserving analysis tool; we ran the data collection on their behalf, and they conducted their own independent analysis. In this post, we share high-level results from those studies and what we learned running this pilot. We’re also providing an expression of interest form for researchers who may want to work with us in the future.
Ensuring the transition to transformative AI goes well requires understanding its impact on people and society. Right now, data on real-world interactions with AI is concentrated in a handful of labs. We think it would be good if more data was made widely available—to researchers, policymakers, and the general public.
Researchers outside the labs have two options. They can draw on analyses the labs publish, which reflect real usage but often answer the lab’s questions, rather than their own. Or they can use public datasets, which they can study however they like, but skew toward more casual use, and may not reflect how most people actually use AI. Neither is sufficient for independent research on how AI is actually being used.
This spring, we piloted a program in which three external research institutions designed and ran their own studies on Claude usage data through Anthropic Insights (formerly named ‘Clio’), the privacy-preserving tool our own teams use to analyze usage patterns across millions of Claude conversations. We hope to scale this program in the future, so we also conducted an additional privacy audit of all data shared with third-party researchers to verify that our privacy protections held (see Appendix).
We believe this is the first time external researchers have run public independent studies on an AI company's own usage data. Below, we discuss what the external teams found, what we learned running the pilot, and what we are weighing as we decide how to expand the program more widely. We are also publicly releasing the aggregate data from each project.
What the researchers learned
We partnered with three research groups: the Social and Language Technologies (SALT) Lab at Stanford University, the Human Information Processing Lab at the University of Oxford, and METR, a non-profit organization that evaluates frontier AI models. Each group developed its own research questions and used Anthropic Insights to conduct privacy-preserving analysis of roughly 250,000 Claude.ai or Claude Code conversations from April-May 2026.
We wanted our external partners to have as much independence as possible, so our contractual review rights were limited to user privacy, information that could help people violate our usage policies, Anthropic’s confidential information, and research accuracy. Anthropic otherwise had no say in the content of the findings and the researchers are free to publish their results even if they are inconvenient for Anthropic. Below are some early results. We're excited about the directions, and about what others will find now that the data is public.
The Social and Language Technologies Lab studied how humans collaborate with AI. They looked at what types of work people bring to AI, what roles humans retain in completing that work, and where human-AI collaboration breaks down. They found:
- People bring high-stakes work to AI more than expected. Prior research suggested people mostly delegate low-accountability tasks to AI and keep consequential tasks (that is, work that affects others or is hard to undo) for themselves. But the SALT Lab found that over half of Claude conversations involved people delegating consequential tasks to AI. People were most likely to bring consequential work to Claude when seeking professional guidance, particularly on legal or financial questions.
- People usually direct and oversee the work when they collaborate with Claude. In nearly three-quarters of conversations, people set the direction while Claude assisted, and they usually adapted its output rather than using it verbatim. But even when directing Claude on the output they want, people vary in how much they understand and learn from what Claude produces.
- It is common for people to experience friction when collaborating with AI. However, that friction is often productive. The time and effort that people put into seeing how Claude attempts a task, identifying where the request was unclear or misunderstood, and iterating on their direction leads to better results–it pushes people to clarify their intent, refine the output, or stay engaged with the problem.
Read their full writeup here.
The Human Information Processing Lab is studying how people feel while using Claude and how that relates to Claude’s behavior. Their early results indicate:
- How people feel when using AI is linked to how AI behaves. The researchers found patterns of human and AI behavior appeared together in conversations: Claude being warm went together with people being more positive. Claude refusing or disagreeing went together with people pushing back. Claude being eccentric went together with people getting more intellectually engaged. And Claude simply helping went together with people seeming satisfied.
- People’s experience when using AI looks a lot like it does on the rest of the web. The researchers found that the patterns among states like absorption, frustration, and enjoyment in Claude conversations closely resemble those in a separate study on everyday internet browsing, suggesting similarities in how people engage with AI and with other digital activity.
They are still completing their writeup. When it is public, we will add a link to it here.
METR is estimating real-world productivity gains from coding agents and how these increases in productivity change across model generations. Their analysis of Claude Code conversations is still underway, but early results suggest:
- More capable models may save users more time. METR compared Claude’s guesses on how long tasks would have taken without AI to how long they actually took with different Claude models. Their preliminary findings indicate newer models deliver significant speedup over older models. METR plans on sharing more as their analysis develops.
- AI can estimate time taken reasonably well. Because the analysis relies on Claude judging how long a task would take, METR compared those judgments to known completion times from a prior developer study. Claude's estimates correlated with the actual time taken by developers.
- Next: measure how much AI accelerates research. METR is continuing to investigate how their study can provide insight into how much AI speeds up researchers’ work, which could become increasingly important as AI takes on more of its own development.
They are still completing their writeup. When it is public, we will add a link to it here.
What our team learned
Sharing usage data is largely unprecedented in AI, so this pilot was as much an experiment in running such a program as it was a way to enable third-party research in a privacy-preserving way. Protecting our users’ privacy and the researchers’ independence were both paramount, and we achieved both. Anthropic Insights is designed for this—researchers never accessed raw conversations, only aggregated outputs after the same legal and privacy review as our internal work. However, all of this made the pilot slow for an AI lab’s normal research speed and resource intensive to run. Both factors present a challenge to effectively scaling it. For more details on how we ran this pilot, see the Appendix. Below we discuss what we learned and how we addressed the challenges that arose.
It is valuable to pursue the same problem from different perspectives. Some of our partners’ research questions overlapped with work being pursued internally. For example, METR’s proposal was similar to our economics research on “Agentic coding and persistent returns to expertise.” We found this overlap valuable: it gave external researchers the chance to examine similar data and draw their own conclusions. Whether those align with ours is something we’ll follow as their study continues. We also connected METR with our Economics team and found that this connection improved both research teams’ work.
Research methods that work internally need to adapt for external partners. When using Anthropic Insights, a researcher writes a question such as, “What type of guidance is this person asking for?” and Claude answers it for every conversation in the study. The answers are then aggregated into categories; researchers only see final categories and the percentage of conversations that fall under each one. Because we are relying on Claude’s judgments, the tool is sensitive to a question’s wording; a poorly phrased one can place conversations into categories that misrepresent them. Because no one can read the underlying conversations, these errors are hard to catch.
Internally, we manage this by iterating on the questions many times over weeks. External partners couldn’t do that, since repeated privacy review before sharing each dataset would have made the study infeasible. Instead, we had them test their questions on WildChat, a public dataset of human-AI conversations where they could check the answers against the underlying conversations themselves. But WildChat skews toward casual and creative use, unlike Claude traffic, so some questions that performed well on WildChat produced misleading categories once applied to actual Claude conversations. We addressed this by providing guidance on how to interpret Anthropic Insight’s outputs (see Appendix). Going forward, we are exploring how external researchers can develop their questions and categories more effectively in advance.
Maintaining transparency about misuse without enabling it. Some categories in our partners’ Anthropic Insights outputs surfaced violations of our Acceptable Use Policy or Terms of Service—for instance a category of people seeking guidance on a prohibited activity. We think the public should know about misuse of our platform, so we shared most of these violations. The exceptions were categories that described how users got around our safeguards rather than what they attempted. Less than 5% of categories and conversations were affected in each study, and in each case we told researchers which clusters we had altered or removed and why. As a standard practice, when Anthropic Insights surfaces such violations, we share the aggregated data with our Safeguards team for their review. This is also an important process for our work with external researchers moving forward.
Looking forward
Understanding AI’s effects on society is too big a job for AI companies alone. Real oversight needs external researchers asking their own questions of real-world usage data and publishing what they find independently.
This pilot was an experiment: could external researchers conduct independent studies on our platform without compromising our users’ privacy? The effort was more challenging than we expected, and we learned many lessons, but so far the answer seems to be yes. Our partners pursued research we would not have thought to design ourselves, and each told us something new about AI’s real-world impacts. This is a promising first step, but there is far more to do.
The next step is for us to determine whether we can scale this program, both in what kinds of studies we can support given the constraints described above, and in how many we can run at once. We are starting slowly to ensure privacy, safety, and research quality. We want to gauge interest and understand what researchers would want to study. If you are a researcher and access to Anthropic Insights would let you pursue work you cannot do today, please fill out this form.
Appendix
The full appendix is available here. It describes how we ran the program, including how we chose our three partners, the research primer we wrote to explain the program’s goals and what Anthropic Insights can do, and how each project moved from proposal to study design to analysis. It also includes details of our collaboration agreements, which explicitly say our partners are free to publish findings even when they are inconvenient for Anthropic. Furthermore, we cover the third-party privacy audit of this data, conducted by Imperial College London, and the privacy threat model we hold all released data to. We also include guidance on interpreting the data we are releasing from our partners’ Anthropic Insights research studies.
Contributions and acknowledgements
Kunal Handa led the project, collaborated with the partners on their research proposals, drafted research guidance and the contract, ran partners’ Anthropic Insights studies, helped build the technical infrastructure supporting partners’ research, contributed to the internal review of the Anthropic Insights outputs, and wrote the blog post. Miranda Zhang coordinated the partnerships and communications, and contributed to all parts of the work. Gabriel Nicholas coordinated the third-party privacy audit, internal review of Anthropic Insights’ outputs, and contributed to all parts of the work. Miles McCain wrote the guidance on interpreting Anthropic Insights outputs, developed technical infrastructure to support partners’ research, contributed to the internal review of the Anthropic Insights outputs, and provided feedback on the blog post and privacy threat model. Ryan Heller contributed technical infrastructure to support partners’ research. Saffron Huang contributed to the internal review of the Anthropic Insights outputs and provided key feedback and discussion. Thomas Millar and Suzanne Wang contributed technical infrastructure to support partners’ research and to the internal review of the Anthropic Insights outputs. Shan Carter, Mo Julapalli, Matt Kearney, Sarah Pollack, and Judy Shen contributed to the internal review of the Anthropic Insights’ outputs. Matthew Jagielski contributed to the privacy threat model. Shaoyi Zhang contributed technical infrastructure to support partners’ research. Heather Whitney, Ankur Rathi, Aisling Keenan, and David Saunders provided legal and privacy guidance throughout the project. Jake Eaton and Sylvie Carr contributed to the framing and writing of the blog post. Jack Clark and Michael Stern provided valuable guidance, support, and discussion throughout the process. Deep Ganguli provided detailed guidance, organizational support, and feedback throughout all stages of the project.
Additionally, we thank Miriam Chaum, Ishita Dasgupta, Esin Durmus, Adam Farina, Zoe Hitzig, Jerry Hong, Devin Kuokka, Hendson Lin, Maxim Massenkoff, Peter McCrory, Maryam Quasto, Nitarshan Rajkumar, Amie Rotherham, Divya Siddarth, Taylor Sorensen, Jerome Swannack, Alex Tamkin, Molly Villagra, Scott White, and Charles Yang for their helpful ideas, discussion, feedback, and support.
For their partnership in this program, we thank Vishakh Padmakumar, Yijia Shao, Jennifer Wang, Diyi Yang, and Dora Zhao from the Social and Language Technologies Lab at Stanford, Tsvetomira Dumbalska, Hannah Rose Kirk, and Christopher Summerfield from the Human Information Processing Lab at Oxford, and Joel Becker (now at Anthropic), Daniel Paleka, and Parker Whitfill from METR.
For conducting the third-party privacy audit, we thank Zexi Yao, Bozhidar Stevanoski, Peter Romov, Euodia Dodd, Xiaoxue Yang, and Nataša Krčo.
Citation
@online{handa2026enablingindependentresearch,
author = {Kunal Handa and Miranda Zhang and Gabriel Nicholas and Miles McCain and Ryan Heller and Saffron Huang and Thomas Millar and Suzanne Wang and Shan Carter and Mo Julapalli and Matt Kearney and Sarah Pollack and Judy Shen and Matthew Jagielski and Shaoyi Zhang and Heather Whitney and Ankur Rathi and Aisling Keenan and David Saunders and Jake Eaton and Sylvie Carr and Jack Clark and Michael Stern and Deep Ganguli},
title = {Enabling independent research on how people use Claude},
date = {2026-08-26},
year = {2026},
url = {www.anthropic.com/research/enabling-independent-research},
}
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