Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being

TL;DR

This paper introduces Human-Layer AI, a user-controlled, explainable interface layer for social media that enhances safety, autonomy, and well-being through five innovative pattern frameworks and a practical Chrome/Edge prototype.

Contribution

It presents a novel, user-owned AI intermediary with five pattern frameworks and a mathematical model to improve safety and agency on social media platforms.

Findings

Prototype demonstrates technical accuracy and usability.

Users can effectively control content and mitigate harm.

Frameworks improve user well-being and autonomy.

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.