Source URL: https://simonwillison.net/2024/Dec/12/clio/#atom-everything
Source: Simon Willison’s Weblog
Title: Clio: A system for privacy-preserving insights into real-world AI use
Feedly Summary: Clio: A system for privacy-preserving insights into real-world AI use
New research from Anthropic, describing a system they built called Clio – for Claude insights and observations – which attempts to provide insights into how Claude is being used by end-users while also preserving user privacy.
There’s a lot to digest here. The summary is accompanied by a full paper and a 47 minute YouTube interview with team members Deep Ganguli, Esin Durmus, Miles McCain and Alex Tamkin.
The key idea behind Clio is to take user conversations and use Claude to summarize, cluster and then analyze those clusters – aiming to ensure that any private or personally identifiable details are filtered out long before the resulting clusters reach human eyes.
This diagram from the paper helps explain how that works:
Claude generates a conversation summary, than extracts “facets" from that summary that aim to privatize the data to simple characteristics like language and topics.
The facets are used to create initial clusters (via embeddings), and those clusters further filtered to remove any that are too small or may contain private information. The goal is to have no cluster which represents less than 1,000 underlying individual users.
In the video at 16:39:
And then we can use that to understand, for example, if
Claude is as useful giving web development advice for people in English or in Spanish. Or we can
understand what programming languages are people
generally asking for help with. We can do all of this in a really privacy preserving way because we are so far removed from the underlying conversations that we’re very confident that we can use this in a way that respects the sort of spirit of privacy that our users expect from us.
Then later at 29:50 there’s this interesting hint as to how Anthropic hire human annotators to imporve Claude’s performance in specific areas:
But one of the things we can do is we can look at
clusters with high, for example, refusal rates, or trust
and safety flag rates. And then we can look at those and say huh, this is clearly an over-refusal, this is clearly fine. And we can use that to sort of close the loop and say, okay, well here are examples where we wanna add to our, you know, human training data so that Claude is less refusally in the future on those topics.
And importantly, we’re not using the actual
conversations to make Claude less refusally. Instead what we’re doing is we are looking at the topics
and then hiring people to generate data in those
domains and generating synthetic data in those domains.
So we’re able to sort of use our users activity with Claude
to improve their experience while also respecting their
privacy.
Tags: generative-ai, anthropic, claude, ethics, privacy, ai, llms
AI Summary and Description: Yes
Summary: The text discusses Anthropic’s Clio system, which aims to extract insights from user interactions with its AI model, Claude, while prioritizing user privacy. This innovative approach leverages data clustering and synthetic data generation to enhance AI performance without compromising personal information, marking a significant development in the realm of AI privacy practices.
Detailed Description:
The informative piece provides an overview of Anthropic’s Clio system, designed to generate insights from user interactions without violating privacy principles. The focus on privacy is crucial in AI applications, making this development particularly relevant for professionals in AI security, compliance, and data governance. Key points explored include:
– **Purpose of Clio**: The primary goal is to gain insights into Claude’s usability across different languages and programming topics while ensuring that user privacy is maintained vigorously.
– **Mechanism**:
– **User Conversations**: Clio utilizes conversations with Claude to create summaries that help identify patterns in inquiries.
– **Privacy Filtering**: Before analysis, user data is stripped of any personal or identifiable details. The conversation summaries generate “facets,” simplifying the data into broader characteristics such as language and topics.
– **Clustering Approach**: The system creates clusters based on these facets, with strict thresholds in place to ensure no cluster contains fewer than 1,000 users, thus reducing the risk of de-anonymization.
– **Practical Use Cases**: Clio’s analysis allows the team to understand user preferences and effectiveness in various contexts, like comparing the utility of Claude for web development advice in different languages.
– **Human Enhancement of AI**:
– Anthropic’s strategy to refine Claude involves analyzing refusal rates and trust/safety flags. By identifying areas where the AI’s responses are unsatisfactory, they can generate human-annotated synthetic data without using actual user conversations.
– This approach not only improves the AI’s performance but also upholds the integrity of user privacy expectations.
The Clio system epitomizes a progressive step in handling AI data responsibly, serving as an excellent case study for professionals looking to enhance security and compliance measures in AI environments, particularly concerning privacy in model training and performance optimization.
– **Relevance to Privacy**: This initiative ties into the broader discussion of privacy-preserving AI practices, making it an essential consideration for those involved in developing or regulating AI technologies.
– **Impact on AI Security**: By ensuring that personal data is not exposed during the analysis process, Clio aligns with the principles of ethical AI, security, and privacy compliance.
Overall, Clio represents a robust approach to responsibly managing AI interactions and is noteworthy for security and compliance professionals seeking innovative methods to improve system performance while safeguarding user data.