Council of Europe HUDERIA Methodology and Model: COBRA — Interactive Tool

WPF has crafted a digital, interactive version of the Council of Europe's HUDERIA/COBRA assessment which allows for conducting a systematic AI risk analysis and exporting the results; session only, nothing saved. This tool is based on the CoE's official publication of the HUDERIA Methodology and HUDERIA Model: COBRA. WPF has created this tool as a digital public good.

Percolation motif representing AI and quantum systems, World Privacy Forum

Key Reports

Latest

View all

Oral Public Comment by Pam Dixon to U.S. National Geospatial Advisory Committee (NGAC) Regarding Poverty Mapping

Is poverty mapping something one needs to think about? Yes -- there is a poverty prediction scale, and where you live sets the score, one you've likely never seen. On 12 August 2026, World Privacy Forum founder Pam Dixon delivered oral public comments to the U.S. National Geospatial Advisory Committee on the downstream data consequences of poverty mapping — what happens after a place-based estimate of poverty leaves the study and enters eligibility, allocation, and commercial scoring systems — and what happens to the decisions related to those systems. Comments in the full post.

Remarks of Pam Dixon to the Council of Europe's Inaugural Meeting of the Committee on New and Emerging Technologies

WPF Executive Director Pam Dixon delivered these remarks at the inaugural meeting of the Council of Europe's Committee on New and Emerging Technologies (CDNET), 15 April 2026. The remarks examine AI and identity ecosystems in the financial sector — where AML and KYC mandates now operate through continuous behavioral monitoring, or perpetual KYC — and the migration of those techniques into commercial ecosystems as persistent identity, or pID.

Kairan Zhao on the Limits of Machine Unlearning as a Privacy Method

Machine unlearning is a broad category encompassing a variety of techniques intended to remove - or lessen - the presence of sensitive information or concepts such as personal data, intellectual property like branded content and even artistic styles from machine learning models. In the privacy context, machine unlearning is often discussed in relation to the European Union's General Data Protection Regulation (GDPR) and its AI Act, as well as the California Consumer Privacy Act. But the chasm between what some in the policy world might believe machine unlearning can do and the actual technical capabilities of these methods is wide.