Reclaiming Social Media: A New Vision for User Control

by

Author: Denis Avetisyan

This review proposes a framework for building social platforms that prioritize user wellbeing, safety, and agency over pure engagement.

The hierarchical learning agent achieves robust performance through pattern coordination and optimization, establishing a definitive decision flow for complex tasks.
The hierarchical learning agent achieves robust performance through pattern coordination and optimization, establishing a definitive decision flow for complex tasks.

A Human-Layer AI approach with actionable HCI design patterns can empower individuals to navigate and shape their online experiences.

Despite the promise of connection, contemporary social media often prioritizes engagement at the expense of user wellbeing and agency. This paper, ‘Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being’, introduces Human-Layer AI—user-owned intermediaries designed to restore control over online experiences. We demonstrate a functional prototype implementing five design patterns that empower users to rewrite content, assess integrity, curate feeds, manage compulsive behavior, and recover from harassment—all while preserving autonomy through explainable controls. Could this approach offer a viable pathway towards retrofitting existing platforms with a more humane and empowering design?

The Illusion of Control: A Fractured Social Contract

Current social media platforms operate on ‘Simplex Communication’, prioritizing algorithmic control over user agency. This unidirectional flow shapes experiences, leading to filter bubbles and potential manipulation. The system delivers content dictated by algorithms, not user exploration.

This lack of control diminishes ‘User Autonomy’, rendering individuals vulnerable to harmful content and bias. Recent studies show trust in platform-mediated content is only 28%, reflecting skepticism regarding authenticity. This erosion impacts public discourse and informed decision-making.

The lack of transparency fosters distrust, necessitating a new approach. Data suggests 62% of users experience post-session regret, indicating a disconnect between intended engagement and satisfaction. A truly elegant network prioritizes predictable and consistent user control.

Human-Layer AI: Reclaiming Agency in the Digital Sphere

‘Human-Layer AI (HL-AI)’ addresses limitations in current social media safety protocols, prioritizing user agency, wellbeing, and proactive content mitigation. Unlike systems relying solely on algorithmic curation, HL-AI empowers individuals within their online experience.

At its core, HL-AI fosters ‘Duplex Communication’, enabling users to shape their information ecosystem through an intermediary layer for pre-consumption filtering. The system balances ‘Risk Mitigation’ – detecting harmful content – with user freedom. Initial results demonstrate 99% detection of terrorism-related posts before user reports.

The prototype screenshots demonstrate the browser-based intermediary for HL-AI, currently represented as a placeholder.
The prototype screenshots demonstrate the browser-based intermediary for HL-AI, currently represented as a placeholder.

The framework operates as a browser-based intermediary, seamlessly integrating with existing platforms without altering platform algorithms, facilitating rapid deployment. Further research focuses on refining the balance between proactive filtering and user control, and mitigating potential biases.

Architecting Control: The HL-AI Toolkit

The ‘Browser Extension’ is the central interface for HL-AI integration, enabling pattern application without altering core platform code.

Key components include the ‘Pattern Engine’ – executing user-defined interventions – and the ‘Context Monitor’ – assessing when and how to apply them. A ‘Decision Coordinator’ manages conflicts between patterns, prioritizing safety and preferences. The ‘Interface Adapter’ enhances adaptability through independent UI modifications.

Critically, the system incorporates a ‘Transparency Layer’, ensuring users retain full understanding and control over HL-AI actions, preserving autonomy.

From Awareness to Action: Empowering Interventions

HL-AI implements interventions designed to reshape the user experience and mitigate harmful interactions. These patterns include ‘Micro-Withdrawal Agent’ – discouraging compulsive scrolling – and ‘Granular Feed Curator’ – enabling fine-grained content control.

The system assesses content integrity and offers alternatives. A ‘Post Integrity Meter’ provides real-time trustworthiness signals, while the ‘Context-Aware Post Rewriter’ generates AI-assisted revisions.

The example illustrates a context-aware post-rewriter, denoted as P1.
The example illustrates a context-aware post-rewriter, denoted as P1.

For users experiencing distress, ‘Recovery Mode’ creates a safe space, limiting exposure to triggering content.

An ‘Optimization Problem’ continuously refines these interventions, balancing protection with autonomy. This addresses a critical gap, where 38.2% of users share misinformation due to inadequate support. A truly elegant system anticipates disorder and resolves it before it manifests.

The pursuit of a human-centered social media ecosystem, as detailed in the article, demands a relentless focus on provable system behavior. This aligns perfectly with Marvin Minsky’s assertion: “The more we understand about intelligence, the more we realize how much of it is simply pattern matching.” The article’s Human-Layer AI framework seeks to move beyond opaque engagement algorithms and instead build systems where user agency and wellbeing are explicitly modeled and verifiable. Every line of code contributing to this framework must therefore be scrutinized for unnecessary complexity – redundancy introduces potential for unintended consequences and diminishes the provability of desired outcomes. A minimalist, mathematically grounded approach is not merely aesthetic; it is essential for ensuring the safety and autonomy of users within this evolving digital landscape.

What’s Next?

The pursuit of a ‘humanized’ social-media ecosystem, as presented, reveals a fundamental tension. The very notion implies a deviation from optimization – a deliberate introduction of inefficiency to serve ill-defined notions of ‘wellbeing’ and ‘agency’. One wonders if this is not merely replacing one form of algorithmic control with another, cloaked in benevolent intent. The proposed Human-Layer AI, while logically structured, remains reliant on interpretable proxies for complex human states. The true challenge lies not in building more sophisticated interfaces, but in achieving a formal understanding of what constitutes meaningful user control—a mathematically rigorous definition, not a collection of subjective preferences.

Future work must address the inherent difficulty of translating ethical principles into verifiable algorithmic constraints. The presented design patterns offer a starting point, but lack the axiomatic foundation necessary to guarantee desired outcomes. A critical limitation is the assumption that increased transparency automatically equates to enhanced agency. It is entirely possible – indeed, probable – that exposing the underlying mechanics of these systems will simply overwhelm users, leading to learned helplessness rather than informed decision-making.

Ultimately, the field requires a shift in focus. Rather than attempting to ‘augment’ existing platforms, a more fruitful avenue may lie in exploring entirely new architectures – systems built from first principles, where user autonomy is not an afterthought, but a fundamental design constraint. This demands a level of mathematical precision currently absent from the discourse, a willingness to abandon the pursuit of engagement at all costs, and a healthy skepticism towards the notion that technology can ‘solve’ fundamentally human problems.

Original article: https://arxiv.org/pdf/2511.05875.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-11 15:53