The Danish government revealed Thursday that a broad coalition of legislators are working on a bill that would make deepfakes illegal to share and put legal protections in place to prevent AI material depicting a person from being disseminated without their consent. "In the bill we agree and are sending an unequivocal message that everybody has the right to their own body, their own voice and their own facial features, which is apparently not how the current law is protecting people against generative AI," Danish culture minister, Jakob Engel-Schmidt, told The Guardian. The Danish department of culture will submit a proposed amendment for consultation this summer. The bill, if enacted, would issue "severe fines" for online platforms that do not abide by the new law. The Danish government said that parodies and satire would not be affected by the proposed amendment.

The Danish government said on Thursday it would strengthen protection against digital imitations of people's identities with what it believes to be the first law of its kind in Europe. Having secured broad cross-party agreement, the department of culture plans to submit a proposal to amend the current law for consultation before the summer recess and then submit the amendment in the autumn. It defines a deepfake as a very realistic digital representation of a person, including their appearance and voice. The Danish culture minister, Jakob Engel-Schmidt, said he hoped the bill before parliament would send an "unequivocal message" that everybody had the right to the way they looked and sounded. He told the Guardian: "In the bill we agree and are sending an unequivocal message that everybody has the right to their own body, their own voice and their own facial features, which is apparently not how the current law is protecting people against generative AI."

Most content-based websites, like Yahoo News, HuffPost, or any given news site, organize their stories according to subject matter or in some similar way. You can imagine that websites with a huge amount of stories must need an automated method to filter or categorize them as the content is ingested into their systems. For example, algorithms that power Yahoo News label news articles with tags (e.g., Military conflict, Nuclear policy, Refugees) as they are ingested, and then display the content by subject matter and/or on a personalized feed. This well-known process of labeling content with all its relevant tags is known as Multilabel Learning (MLL). Up to now, whenever scientists and engineers use MLL to create their own specific models to label content however they like, they have used datasets that have pre-computed features like bag-of-words, or dense representations like doc2vec.