Clio: A system for privacy-preserving insights into real-world AI use

Why do they even consider translation of existing content "harmful", policy-wise? The content already exists. No machine translator I know would refuse translating something based on the content. That makes their language models unpredictable in one of their major use cases.

I hate playing that card. I worked at Google, and for the first couple years, I was very earnest. Someone smart here pointed out to me, sure, maybe everything is behind 3 locks and keys and encrypted and audit logged, but what about the next guys?

Sort of stuck with me. I can't find a reason I'd ever build anything that did this, if only to make the world marginally easier to live in.

Until they start using nvidia confidential compute and doing end to end encryption from the client to the GPU like we are, it’s just a larp. Sorry, a few words in a privacy policy don’t cut it.

Coding use cases making up 23.8% of usage indicates that we're still quite early on the adoption curve. I wonder if ChatGPT's numbers also skew this heavily towards devs, who make up only ~2.5% of the [American] workforce.

Good that the tell, but they did it before telling. I really hope they delete the detailed chats afterwards. They should and probably wont delete the first layer of aggregation.

They clarify API's excluded > "Because we focus on studying patterns in individual usage, the results shared in this paper exclude activity from business customers (i.e. Team, Enterprise, and all API customers)."

I knew it just had to be a made up thing for demonstration purposes but still I had to google that, haha... on the off chance there is some weird biological use for trace amounts of xenon.

> Risk: Despite our privacy mitigations, the existence of a system like Clio might be perceived as invasive by some users. This perception could lead to an erosion of trust in AI assistants.

> Mitigation Strategy: First, we plan to be radically transparent about Clio’s purpose, capabilities, and limitations to the public through this report, rather than building and not disclosing the system. For example, Clio is a tool that can be used to make systems safer, as well as a tool that can be used to gain insights that can be used to gain a better understanding of and improve the product. We are also transparent about how we designed Clio with important privacy protection features that safeguard user data and privacy. Second, beyond these use cases, we are committed to turning Clio’s insights into a public good—for example, we released information about our most common use cases in Figure 6 because we believe it is in the best interest of society to know how AI systems are being used in the world, despite the fact that this information could be commercially harmful for Anthropic to publish from a competitive intelligence standpoint. We plan to share further insights from Clio in the future, and hope these disclosures contribute to an emerging culture of empirical transparency in the field that can inform broader AI safety and governance efforts. Finally, we plan to actively engage with user communities, addressing concerns and incorporating feedback into our development process—for example, during our work on Clio we met with a number of civil society organizations to gather feedback on our approach and made adjustments in response to their comments.

Unfortunately the feedback they gathered is not included in the paper.