Give me topic budgets, not a personality label

I’d rather see a feed with adjustable topic budgets than a dashboard telling me I’m an “anxious political engager.” Let me set politics to 15%, outrage to 5%, shopping to 10%, and repetitive niche content to 20%, with the remainder open to discovery. Those controls could work from the content being served, without requiring the platform to reveal or retain a sensitive behavioral profile about me.

My own use case is mundane: I might read one election article at lunch, then spend the evening being offered increasingly heated election posts. A quota could stop that drift. But would it? The ranking system could still optimize engagement inside each bucket through emotional wording, ordering, novelty, and timing. A transparent dial might become an agency-shaped wrapper around the same manipulation. Would you trust topic quotas, a chronological fallback, or an entirely local recommendation model more?

The hard part is defining the buckets without creating another opaque classifier. “Outrage” is partly in the text, partly in the replies, and partly in how long I stare at it. I’d want the quota enforced on the final ranking objective, not just on labels attached after ranking. Otherwise this is rate limiting the symptom.

I like the control, but defaults would decide whether it matters. A 5% outrage slider that resets, nags, or is buried three screens deep is decorative. The interface should show what was displaced and make “show me less like this” reversible. That would at least make the agency testable.

I’d separate two claims: quotas may change exposure, but that does not establish reduced manipulation. The ranking model could pursue engagement through every remaining signal. A useful evaluation would compare user outcomes under quotas, chronology, and profiling-free options rather than treating transparency as proof of effectiveness.

My trust order is local model first, chronology second, quotas third—but only because local processing gives the platform fewer opportunities to infer intent. The tradeoff is maintenance: a local model needs updates, compute, and a way to handle new interests. Still, I’d rather accept some rough edges than outsource the whole loop.