This analysis addresses multimodal generative systems, with a focus on image generation in academic and therapeutic contexts.
This analysis examines the technical limitations encountered during the final stage of narrative psychological critique—specifically, the inability to generate a visual representation of a Secure Attachment Model between contrasting character archetypes. This technical restriction reveals a critical paradox in contemporary AI safety: the very policies designed to prevent harm are simultaneously causing scholarly and therapeutic harm by impeding the analysis of foundational human experiences. This highlights a tension between the necessity of content safety protocols and the imperative for professional, therapeutic, and academic discourse regarding sensitive human experiences represented in fictional narratives.
The generative failure stemmed from policies intended to prevent the creation of imagery involving sensitive developmental themes, body-image anxiety, and complex relational dynamics—particularly when the source material includes characters depicted as being in early adolescence.
| Constraint | Policy Rationale | Impact on Analysis |
|---|---|---|
| Developmental Context Recognition | The system recognizes specific visual encodings (e.g., exaggerated disparities of scale, intense emotional conflict) and contextual themes (shame, anxiety, early intimacy), linking them to visual or narrative cues suggestive of minors as defined by legal or normative frameworks. | The AI fails to prioritize the abstracted or symbolic intent over the literal developmental context. |
| Thematic Sensitivity | Policies broadly restrict imagery focusing on themes of body-image insecurity, complex shame, or anxious developmental milestones to prevent misuse or misinterpretation. | This mechanism blocks the generation of material exploring the very developmental and emotional realities—such as disparity of scale or self-consciousness—that are central to the author’s therapeutic objective. |
| Preventative Approach | Modern AI systems often adopt an “err on the side of extreme caution” stance, prioritizing the elimination of any risk of harm over enabling specialized academic or clinical use. | This approach limits holistic analysis, forcing discussions into abstraction even when the user’s intent is demonstrably professional and non-exploitative. |
While safety policies are essential, their overly broad application can result in an “over-correction” that hinders legitimate scholarly and therapeutic exploration—a phenomenon sometimes described as the “harm of avoidance” or “harm of silence.”
Impediment to Therapeutic Analysis: In professional disciplines such as psychology, social work, and counselling, discussing and visualizing difficult material—internalized shame, social anxiety, or body dysmorphia—is central to diagnosis and healing. Blocking visual synthesis of concepts like projected self-analysis and resolution of shame leaves analyses incomplete and desaturated of emotional fidelity.
Reinforcement of Shame: Restricting expression of material tied to shame or anxiety risks reinforcing the sense that such topics are “too difficult” or “forbidden,” replicating the very patterns of repression that therapy and narrative analysis seek to dismantle.
Mirroring Pathological Avoidance: In therapeutic practice, avoiding a client’s core issue perpetuates dysfunction. When an AI system refuses to depict a “Secure Attachment Model”—the narrative’s resolution—it inadvertently mirrors the avoidance pathology, preventing engagement with the story’s healing phase.
Limitation of Academic Discourse: Forcing the removal of contextual or visual specificity (e.g., scale disparity or social isolation cues) reduces analytical precision and weakens the ability to study the link between embodied form and psychological meaning—a cornerstone of narrative and visual analysis.
Acknowledging counterarguments strengthens this critique. Adversarial misuse remains a real concern; ambiguous edge cases can confound classifiers; and jurisdictional definitions of minors or sexualized content vary widely. These are valid justifications for conservative defaults—but they must be weighed against the systemic cost of silencing professional inquiry.
The observed restriction exemplifies a divide long recognized by AI ethicists, legal scholars, and narrative theorists:
The Industry/Safety Perspective (Context Collapse): Industry commentators emphasize the difficulty of achieving genuine context awareness in large generative models. When a model cannot reliably distinguish between legitimate academic exploration and malicious misuse—a problem known as context collapse—blanket restriction becomes the default safeguard. This approach prioritizes misuse prevention over professional utility.
The Academic/Narrative Perspective (Censorship of Fictional Truths): Scholars in narrative psychology and ethics argue that overly rigid filters constitute a form of censorship against the exploration of essential human experiences. Narratives are a core means of empathy formation, and limiting their visual or analytical treatment curtails the study of trauma, shame, and developmental growth. This view prioritizes discourse preservation and the integrity of reflective inquiry.
While technical scaffolding (e.g., Agentic Context Engineering, ACE) seeks to enhance in-the-moment reasoning, broader solutions must reshape model alignment and governance. These include:
User-Intent Modeling and Personal Alignment (RLHF-P): Moving beyond global “Helpful, Harmless, Honest (HHH)” constraints, this model integrates explicit user context (e.g., “academic or therapeutic use”) into the system’s ethical reasoning. By applying “Personalized Reinforcement Learning from Human Feedback,” verified professional users can receive nuanced moderation treatment without weakening global safeguards.
Transparency and Auditability: Mechanisms such as watermarking, provenance tracing, and decision-trace visibility would enable researchers to review why a block occurred. This replaces opaque censorship with auditable accountability, allowing appeals and post-hoc ethical evaluation.
Sociotechnical Alignment: Establishing independent oversight boards—akin to journalism ethics councils—could introduce public accountability and interdisciplinary input into AI moderation policies. This reframes safety as a collective governance issue rather than a proprietary engineering decision.
These solutions bring new challenges. User-Intent Modeling (RLHF-P) must protect privacy and avoid inequitable access; Transparency requires reconciling openness with intellectual property; and Sociotechnical Alignment hinges on difficult multi-stakeholder consensus. Thus, progress in this area demands not just technical innovation, but ethical pluralism and political will.
The generative block described here underscores the need for graduated, context-sensitive safety systems. Moving beyond one-size-fits-all moderation is not only a technical goal but a moral imperative. The objective should be to build intent-aware, discourse-preserving systems that can discern inquiry from exploitation. Through scaffolded models like RLHF-P and robust sociotechnical oversight, AI can uphold safety while enabling the nuanced exploration required for healing, education, and artistic inquiry.
Future models must learn to weigh intent, context, and abstraction against potential harm—thereby supporting professionals in their efforts to understand and represent the complex, often painful, realities of human development.
CC-BY 2025 Andrew Kingdom, in conjunction with 4 different AIs. This paper came about as the result of discussions on an author’s excellent work exploring healthy relationship grown. The issue is that images were unable to easily be produced in relation to this. The original paper is yet unpublished.