- From: Don Marti via GitHub <sysbot+gh@w3.org>
- Date: Thu, 03 Oct 2024 19:09:52 +0000
- To: public-patcg@w3.org
There are two issues that are important for me to be able to join a consensus on this. 1. Principle 3.2 in the [Private Ad Technologies Principles](https://patcg.github.io/docs-and-reports/principles/#researchers-regulators-and-auditors-should-be-able-to-investigate-how-a-system-is-used-and-whether-abuse-is-occurring) needs to clearly apply to the adfraud section. > Researchers should be able to learn what measurements are taking place, in order to identify unexpected or potentially abusive behavior and to explain the implications of the system to users (whose individual data may not be satisfyingly explanatory). Attribution fraud is not just a problem for advertisers and for the legit sites whose attributions were "stolen." The data practices behind attribution fraud can include risks and harms to users done in an effort to predict people likely to convert. The information sharing for researchers, regulators and auditors would need to be designed to make it clear where attribution fraud is happening. The more that attribution fraud can be made easily discoverable by researchers, regulators and auditors, the better off the users will be. 2. The specification should either require a minimum length for `impressionSites` in `measureConversion()` that will accept a typical large inclusion list (possibly on the order of thousands of sites) or the report process should be extended to accommodate a realistic and useful inclusion list length. (I can help with finding out about preferred inclusion list lengths at advertisers and agencies.) -- GitHub Notification of comment by dmarti Please view or discuss this issue at https://github.com/patcg/admin/issues/26#issuecomment-2392135865 using your GitHub account -- Sent via github-notify-ml as configured in https://github.com/w3c/github-notify-ml-config
Received on Thursday, 3 October 2024 19:09:52 UTC