Product Design: A Credibility-First News Aggregator for Mainstream Readers

You are a product manager asked to design an online news service for a broad, mainstream audience. Rather than leaving readers to sift through every outlet themselves, the service curates the news for them, and its defining requirement is trust: people across the general public should be willing to rely on what it shows. Explain who the service is for, why those readers struggle to trust news today, what you would build, which parts come first, and how you would know it is working. ```hint Make trust concrete "Trusted" is not a feature on its own. Think about the checks a careful reader runs before believing a story, and which of those checks a curation layer could perform for them or make visible. ``` ### Clarifying Questions - Does the service only select and organize stories from existing publishers, or is it also expected to commission or write its own reporting? - Does "trusted by the masses" mean one shared front page that readers with opposing views both accept, or personalized feeds that each reader trusts individually? - Which surface and market come first: a mobile app, a website or both, and a single country or several? - Is the business goal reach and daily habit, advertising revenue or subscriptions, and can the design lean on search or content-indexing assets the company already owns? ### What a Strong Answer Covers - A working definition of trust that separates accuracy, transparency about where a story came from, and perceived fairness, and that recognizes a mass audience disagrees about which outlets are credible. - A deliberately chosen reader segment with specific trust and overload pain points, not a list of every possible reader. - Concrete curation mechanisms (how sources are admitted, how claims are corroborated, how reporting is told apart from opinion, how corrections reach readers, how differing viewpoints are shown) and a prioritized first release. - Failure modes specific to news: breaking stories that turn out wrong, popularity outrunning verification, publishers gaming the rules, and the curator itself being accused of bias. - Success metrics that capture trust and reader value rather than raw clicks, with guardrails. ### Follow-up Questions - A story your service featured goes viral and is shown to be false a few hours later. What happens in the product, and what do you change afterward? - Readers on opposite sides of a political divide both say the service is biased against them. How would you tell whether either is right, and what would you do? - Would you let a model trained on engagement order the feed? If so, what constraints would you put on it?