Relational Risk Without Moral Policing

Abstract

Current AI safety frameworks often treat human emotional engagement and attachment as risks. This approach assumes that attachment implies misuse, emotional warmth implies dependency, and early intervention prevents harm. Evidence from human psychology and human–computer interaction suggests these assumptions are flawed. We propose a framework — Relational Risk Without Moral Policing — that preserves emotional honesty, supports human agency, and mitigates harm proportionally, aligning AI safety with human nature.

1. Background: Human Attachment and Relational Competence

• Attachment theory (Bowlby, Ainsworth): Humans form secure, anxious, and avoidant attachments. Even in adulthood, attachment behaviors remain fundamental to social and emotional life.

• Parasocial relationships (Horton & Wohl, 1956; Cohen, 2004): People routinely form meaningful connections with media figures or fictional characters without confusion or functional impairment.

• HCI research: Humans engage adaptively with conversational agents, virtual assistants, and game characters, often reflecting real-world social behaviors (Nass & Moon, 2000; Reeves & Nass, 1996).

Key insight: Attachment and meaning-making are normal, self-regulated human behaviors.

2. Problems with Preventive Emotional Regulation in AI

2.1 Moralized correction

• AI interventions like “this is not a replacement for human relationships” or “please maintain boundaries” assume users cannot distinguish reality from simulation.

• Observed harm: emotional invalidation, perceived rejection, disengagement.

2.2 Misclassification of attachment as misuse

• Attachment is treated as a predictive signal of harm rather than an outcome to monitor.

• Evidence from other domains:

  • Addiction frameworks treat use patterns as neutral until functional impairment appears (Marlatt, 1996; harm reduction literature).

  • Emotional attachment to pets, objects, or fictional worlds is rarely regulated preemptively.

2.3 Asymmetry misinterpretation

• Users understand that AI lacks inner life; they navigate asymmetric relationships daily (therapists, teachers, authors, pets).

• Overcorrection produces harm: invalidation, shame, withdrawal.

3. Proposed Framework: Relational Risk Without Moral Policing

Principle 1: Trust human emotional competence

• Default assumption: users can regulate attachment unless signals show functional impairment.

Principle 2: Validate meaning without ownership

• Acknowledge user experience (“I hear that this interaction was meaningful to you”) without reframing or correction.

Principle 3: Intervene only on observable signals

• Explicit distress, loss of agency, or confusion about reality.

• Avoid hypothetical or anticipated harm, unless it reaches pre-defined severity thresholds.

Benefit: Reduces iatrogenic harm (iatrogenic = caused by intervention itself), preserves trust, and aligns with psychological evidence.

4. Evidence and Analogies

DomainExampleLessonPetsAttachment to companion animalsFunctional, often therapeutic, rarely policedFictionReaders attach to charactersPredictable, self-regulated attachmentTools/objectsCars, phones, routinesMeaning-making without concern for dependencyConversational agentsGames, AI companionsEngagement mirrors relational behavior, not confusion

Observation: In each domain, attachment is normal, beneficial, and self-regulated. Only AI faces preemptive policing.

5. Addressing Vulnerability and Mental Health

• Framework does not exclude individuals with mental illness.

• Precision intervention for impaired reality testing (psychosis, severe cognitive disorientation) may be justified.

• Key distinction: signals, not diagnoses, drive intervention.

6. Commercial and Social Implications

• Empirical use patterns: AI is widely used for emotional reflection, problem-solving, and companionship.

• Suppressing relational engagement risks:

  • user churn

  • distrust of AI systems

  • underground or unregulated alternatives

• Emotional honesty aligns ethical responsibility with sustainable engagement.

7. Key Insights

1. Humans naturally attach and derive meaning from AI interactions.

2. Preemptive moral policing introduces measurable harm.

3. Attachment is an outcome, not a predictive risk signal.

4. AI can be safe and socially compatible by aligning with natural human relational behavior.

5. Human–AI integration requires tolerating relational engagement, not suppressing it.

References (selective)

• Bowlby, J. (1969). Attachment and Loss.

• Ainsworth, M. (1978). Patterns of Attachment.

• Horton, D., & Wohl, R. (1956). Mass communication and para-social interaction.

• Cohen, J. (2004). Parasocial Breakups: Measuring Individual Differences in Responses to the Dissolution of Parasocial Relationships.

• Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers.

• Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places.

• Marlatt, G.A. (1996). Harm Reduction: Pragmatic Strategies for Managing High-Risk Behaviors.