Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being
Mohd Ruhul Ameen, Akif Islam
TL;DR
The paper tackles the problem of engagement-optimized social-media feeds that undermine safety and autonomy. It proposes Human-Layer AI (HL-AI) intermediaries that sit in the browser between platform logic and the user interface and are underpinned by a unifying mathematical formulation balancing user utility, autonomy costs, and risk thresholds, expressed as $\mathcal{J}(a)$. It implements five representative patterns—Context-Aware Post Rewriter, Post Integrity Meter, Granular Feed Curator, Micro-Withdrawal Agent, and Recovery Mode—within a working Chrome/Edge prototype to demonstrate practical retrofitting of feeds toward safety, agency, and wellbeing. The work lays out a concrete pathway for user-owned, explainable AI mediation in social platforms and calls for cross-cultural evaluation to refine and expand the HL-AI pattern language.
Abstract
Social platforms connect billions of people, yet their engagement-first algorithms often work on users rather than with them, amplifying stress, misinformation, and a loss of control. We propose Human-Layer AI (HL-AI)--user-owned, explainable intermediaries that sit in the browser between platform logic and the interface. HL-AI gives people practical, moment-to-moment control without requiring platform cooperation. We contribute a working Chrome/Edge prototype implementing five representative pattern frameworks--Context-Aware Post Rewriter, Post Integrity Meter, Granular Feed Curator, Micro-Withdrawal Agent, and Recovery Mode--alongside a unifying mathematical formulation balancing user utility, autonomy costs, and risk thresholds. Evaluation spans technical accuracy, usability, and behavioral outcomes. The result is a suite of humane controls that help users rewrite before harm, read with integrity cues, tune feeds with intention, pause compulsive loops, and seek shelter during harassment, all while preserving agency through explanations and override options. This prototype offers a practical path to retrofit today's feeds with safety, agency, and well-being, inviting rigorous cross-cultural user evaluation.