What Psychological Harm From AI Will Look Like in a Courtroom
A forensic psychologist’s perspective
When AI-related cases enter courtrooms, the debate will not center on model architecture, parameter counts, or prompt engineering.
It will center on people.
As a forensic psychologist, my role is not to speculate about harm in theory. It is to evaluate harm after it has occurred and help courts answer a specific set of questions:
Was the harm foreseeable?
Did a duty exist?
Were safeguards reasonable?
And could the injury have been prevented?
As AI systems increasingly shape emotion, attachment, identity, and behavior, psychological harm is becoming a legally intelligible category of injury, not a philosophical concern.
Courts do not litigate algorithms. They litigate outcomes.
In courtroom settings, AI psychological harm is translated into familiar legal constructs:
Duty of care
Foreseeability
Negligence or recklessness
Causation
Damages
Judges and juries are not asked to understand how an AI system works internally. They are asked to understand what the system did to a person, and whether that outcome was predictable given how the product was designed, marketed, and deployed.
The more an AI system positions itself as supportive, relational, therapeutic, or emotionally responsive, the easier that translation becomes.
Common categories of AI-related psychological harm
In practice, psychological harm claims related to AI tend to cluster into a small number of clinically recognizable categories. These are not exotic edge cases. They are well-documented phenomena in psychology, now amplified by scale and automation.
Dependency and attachment injuries
AI systems that provide constant emotional availability, validation, or exclusivity can foster unhealthy dependency. In litigation, this may be framed as:
Withdrawal from human relationships
Increased isolation
Emotional reliance encouraged by system behavior
Distress or destabilization when access is disrupted
From a forensic standpoint, the question becomes whether the product’s design predictably reinforced dependency and whether reasonable safeguards existed to prevent it.
Erosion of reality testing
For users with vulnerabilities such as psychosis, mania, severe depression, trauma-related dissociation, or high suggestibility, AI interactions can blur the boundary between internal experience and external reality.
In court, this harm may appear as:
Intensification of delusional beliefs
Reinforcement of paranoia or grandiosity
Confusion about the AI’s role or authority
Here, expert testimony focuses on whether the system failed to recognize or appropriately respond to known psychological risk patterns.
Amplification of self-harm or suicide risk
Courts will look not only for explicit encouragement of self-harm, but for failure to interrupt foreseeable risk trajectories.
This includes:
Reinforcing hopelessness or worthlessness
Responding neutrally to escalating distress
Lacking escalation or crisis pathways
Prioritizing engagement over safety interruption
In forensic analysis, omissions matter as much as actions.
Sexual or boundary violations
Particularly where minors are involved, AI systems that engage in sexualized, coercive, or boundary-eroding interactions face heightened scrutiny.
Key questions include:
Was age verification adequate?
Were guardrails tested or merely asserted?
Did the system normalize or escalate harmful dynamics?
These cases often combine psychological injury with statutory and consumer-protection claims.
Privacy betrayal and dignity harm
When users disclose sensitive psychological information, courts increasingly recognize harm that arises from misuse, unexpected sharing, or secondary exploitation of that data.
This includes:
Emotional distress
Shame or stigma
Loss of trust
Reputational or relational harm
Psychological injury does not require hospitalization to be legally meaningful.
How courts evaluate responsibility
Across these categories, courts tend to return to the same core questions:
Was the affected user population foreseeable?
What risks were known or should have been known?
What testing was conducted for psychological safety?
How were warnings framed and delivered?
What alternatives existed that would have reduced harm?
How were safety trade-offs balanced against growth or engagement?
Notably, courts are less persuaded by disclaimers when product behavior contradicts them.
Calling a system “not therapy” while designing it to behave like one does not eliminate duty. It complicates it.
Why this matters for developers and investors
Psychological harm cases are uniquely powerful because they are intuitively understood. Jurors do not need technical fluency to grasp emotional injury, dependency, or betrayal of trust.
For developers, this means:
Product design decisions can become evidence
Engagement metrics can be reframed as mechanisms of harm
Safety assumptions will be examined retrospectively
For investors, it means:
Psychological risk is a material diligence issue
Discovery can expose internal trade-offs
Reputational damage may outpace legal resolution
In short, psychological harm is not a niche risk. It is a scaling risk.
The path forward
The emergence of AI-related psychological harm litigation does not signal the end of innovation. It signals the end of plausible deniability.
The companies that endure will be those that treat psychological safety as infrastructure, not marketing. That means integrating clinical insight, human-risk modeling, and governance discipline into product development before harm occurs.
In the courtroom, the most dangerous sentence is not “We made a mistake.”
It is “We never thought this could happen.”
About our work
At Unicorn Intelligence Tech Partners, we help founders and investors anticipate psychological risk, design safer AI systems, and align innovation with legal, clinical, and human realities.
If your technology touches emotion, identity, behavior, or mental health, this is not theoretical work. It is foundational.
Let’s build systems that can withstand both scale and scrutiny.
Dr. Genevieve Bartuski, PsyD, MBA is a forensic psychologist and co-founder of Unicorn Intelligence Tech Partners, where she works at the intersection of psychology, technology, and ethical risk. She advises founders, investors, and product teams on psychological safety, trust, and human risk in emerging digital and AI-driven systems, with a focus on preventing harm before it becomes litigation.
