Unchecked AI-enabled manipulation risks transforming digital society into a behavioral marketplace where human agency is slowly eroded.

The Case for Regulating AI-Enabled Manipulation

By Abdelrahman Gamal Yacoub – SSRN, 2025, analyzed by ChatGPT-4o

Introduction: A New Frontier in AI Risk

Abdelrahman Gamal Yacoub’s The Case for Regulating AI-Enabled Manipulation is a powerful legal and philosophical examination of how artificial intelligence magnifies one of humanity’s oldest tools: manipulation. The paper offers a compelling normative justification for the inclusion of manipulation as a prohibited "unacceptable risk" under the EU’s Artificial Intelligence Act (AIA), and explores how AI has transformed the scale, precision, and impact of manipulative practices in ways existing legal systems struggle to contain.

This essay reviews Yacoub’s arguments, highlights the transformative nature of AI-driven manipulation, and concludes with targeted recommendations for AI developers, regulators, and enterprise users.

The Pre-AI Landscape: Why Manipulation Wasn’t Previously Regulated

Historically, manipulation—despite being widely regarded as ethically questionable—was not typically treated as a legal offense. This is largely due to its social ubiquity and subjective nature. Manipulation is so embedded in human interaction (e.g., advertising, politics, sales) that it was viewed as a form of persuasion with unclear boundaries and often accepted as part of daily life. Moreover, attempts to legally define manipulation have been hindered by its inherent vagueness and overlap with free speech concerns.

Yacoub insightfully compares the regulability of manipulation to lying: while both can be harmful, lying is often easier to prove because it involves a falsifiable claim. Manipulation, by contrast, is about intentions, perceptions, and effects, which are context-sensitive and harder to pinpoint.

Thus, prior to AI, lawmakers were hesitant to regulate manipulation due to definitional uncertainty, potential overreach, and confidence in existing frameworks (e.g., GDPR, consumer protection laws, and tort law) to address its more egregious forms.

The AI Shift: From Persuasion to Precision Engineering of Human Behavior

AI has revolutionized manipulation in three fundamental ways:

1. Granularity and Simulation of Intent:
   AI systems can analyze vast data sets and simulate human intent, allowing them to engage in dynamic, context-aware, and personalized influence operations. Unlike human persuaders, AI can operate continuously, adaptively, and invisibly, presenting influence not as a single event but as a persistent and evolving ecosystem of nudges.

2. Hyper-Personalization:
   With access to detailed behavioral data, AI systems craft messages tailored to individual cognitive vulnerabilities. Tools such as Large Language Models (LLMs) can understand, simulate, and even steer user preferences. This allows for "market avatars"—behavioral representations of users that can be used to manipulate them with uncanny precision.

3. Scalability and Non-Rivalrous Manipulation:
   AI’s capacity for scale means that manipulation becomes non-rivalrous: influencing one individual doesn’t diminish the system’s ability to influence others. Worse, personalization can amplify societal effects, undermining democratic institutions, fragmenting public discourse, and generating environmental or public health harms without ever appearing as a discrete offense.

Personalization as a Double-Edged Sword

Yacoub convincingly argues that while personalization is a cornerstone of the modern digital economy, it is also the gateway drug to manipulation. AI uses minimal data to infer psychological traits and intent, shaping decisions even without explicit data collection. This raises profound issues:

• Inference vs. Consent:
  Data subjects may consent to data use, but AI systems draw manipulative inferences beyond what is explicitly authorized or even understandable to users. Traditional consent models are largely ineffective in this context.

• Self-Reinforcing Manipulation Loops:
  AI systems extract data, use it to manipulate, and through manipulation elicit more data. This cyclical reinforcement builds behavioral dependency and erodes autonomy.

• Proxy Manipulation Through Group Profiling:
  Even users not individually profiled can be manipulated via group-level psychographic data, bypassing personal data protections entirely.

Societal Harm: From Individual Pressure to Systemic Distortion

A core contribution of the paper is its analysis of manipulation’s shift from personal harm to systemic risk:

• Tragedy of the Commons Effect:
  As manipulation scales, it becomes a tragedy of the commons. Individuals who tolerate personalized nudges contribute to a system that collectively undermines free will, civic participation, and rational discourse.

• Democratic Erosion:
  Filter bubbles and algorithmic bias subtly but powerfully erode informed citizenry. As the paper notes, even minor manipulations of voter behavior via search engine bias can swing election results.

• Public Health and Environmental Misinformation:
  Manipulation techniques are increasingly used to discourage vaccination, downplay climate risks, and promote harmful consumer behaviors. These effects, while diffused and hard to trace, can be catastrophic.

Beyond GDPR: The Need for Tailored Regulation

Although the GDPR, the Digital Services Act (DSA), and the Digital Markets Act (DMA) provide a regulatory baseline, Yacoub shows these frameworks are insufficient:

• GDPR Fatigue and Inefficacy:
  Users rarely read or understand privacy policies. Consent fatigue leads to ineffective oversight, while regulators struggle to respond to systems designed to obscure intent and causality.

• Blurred Lines Between Data and Manipulation:
  The law treats data collection and deployment as a single process, yet manipulation needs to be addressed as a use-based harm independent of collection.

• Lack of Collective Redress:
  Most legal systems focus on individualized harm. The absence of legal pathways for societal-level damage from manipulation leaves a regulatory gap.

Recommendations

For AI Makers:

1. Design for Transparency and Friction:
   Build systems that make manipulation detectable and contestable. Use explainable AI (XAI) principles and integrate user feedback loops to alert users to persuasive tactics.

2. Respect Intentional Boundaries:
   Develop AI that aligns with user-declared preferences, not inferred vulnerabilities. This includes limiting microtargeting, subliminal nudging, and emotion-sensing unless explicitly opted into.

3. Internal Audits for Manipulative Risk:
   Establish internal review boards to examine whether AI outputs subvert user autonomy. Focus on dark patterns, inferred intent shaping, and coercive design.

For Regulators:

1. Separate Data Use From Data Collection:
   Create distinct legal frameworks for the act of manipulation—irrespective of how the data was obtained. Recognize manipulation as an action, not just a consequence of data misuse.

2. Mandate Influence Transparency:
   Require AI systems to declare when they are using manipulative techniques or influencing behavior. Labeling of personalized ads, search rankings, and LLM outputs is crucial.

3. Establish Societal Redress Mechanisms:
   Allow civil society, ombuds offices, or class actions to address harms that manifest at scale but are not individually actionable. Include AI-manipulation within environmental, electoral, and health oversight bodies.

For Corporate AI Users:

1. Audit Supply Chains for Manipulation Risk:
   Firms using AI tools—especially in marketing, hiring, or user engagement—should assess whether vendors’ models use coercive or manipulative design elements.

2. Favor Ethical Personalization:
   Distinguish between helpful personalization and exploitative nudging. Provide users with meaningful controls, opt-outs, and alternatives to manipulative interfaces.

3. Promote Digital Literacy:
   Educate employees and customers about manipulation tactics. Empower users to recognize when AI may be steering them in unseen ways.

Conclusion: A Call for Autonomy-Centric AI Governance

Yacoub’s paper is an urgent reminder that unchecked AI-enabled manipulation risks transforming digital society into a behavioral marketplace where human agency is slowly eroded. Regulating manipulation is not about outlawing influence altogether—it is about ensuring that influence remains visible, contestable, and aligned with the user’s genuine interests.

As AI systems grow in power and pervasiveness, liberal societies must evolve legal and technical safeguards to protect what remains of autonomous choice. Regulation, transparency, and ethical design are no longer luxuries—they are necessities for democratic resilience and personal freedom in the algorithmic age.