Pilot Mental Health
& AI‑Mediated Support
Exploring how pilots navigate mental health support in highly regulated systems — and what makes AI feel trustworthy or risky.
Impact at a glance
Researched
6,000+
survey responses
13 pilot interviews · global aviation community
Reframed
System-level
trust problem
Not individual stigma — regulatory and career risk
Defined
5
AI design principles
Privacy, disclosure safety, and confidentiality boundaries
Influenced
CRMSON
AI mental health platform
Trust-bounded product direction for pilot mental health
Project Overview
Mental health in aviation is not a personal problem.
It's a systems problem.
This research examined how commercial pilots perceive AI-mediated mental health support within environments shaped by medical certification, organizational visibility, and professional risk. The project combined large-scale survey analysis and in-depth pilot interviews to identify the conditions under which AI could support reflection, disclosure, and help-seeking — without increasing fear of surveillance or documentation.
Role
Lead UX Researcher & Project Lead
Timeline
September 2024 – Present
Methods
Survey synthesis · Semi-structured interviews · Thematic analysis · Framework development
Participants
6,000+ aviation survey responses · 13 pilot interviews
Collaborators
Involo CRMSON · University of Washington · University of Melbourne · Southwest Airlines · Delta Airlines
Output
CHI 2026 publication · Trust-bounded AI framework · Design principles for CRMSON
The Core Tension
“Seeking help can feel riskier than staying silent.”
In aviation, mental health disclosure can intersect with medical certification, employer visibility, and career consequences. This makes trust a system-level issue — not only an individual attitude.
01
Certification Risk
“If something goes on your medical record, it can follow you for the rest of your career.”
Pilots routinely weigh personal wellbeing against career survival before every support decision.
02
Informal Preference
“Most pilots talk to other pilots and don't even bother to talk to a doctor.”
Formal medical systems are avoided. Informal peer networks fill the gap — but lack structure and expertise.
03
Constant Trade-offs
“I knew I needed to talk to someone. But I also knew what was at stake if the wrong person found out.”
Pilots describe a continuous cost-benefit calculation that precedes any help-seeking action.
Aviation Mental Health Ecosystem
A system designed around disclosure risk
Mental health support in aviation exists within a regulatory environment where disclosure can carry professional consequences. Trust is not just about the tool — it is shaped by every node in this system. Hover to explore.
Research Approach
Three methods. One converging picture.
01
98%
say mental health is a concern in aviation
Survey Analysis
6,000+ responses
Large-scale aviation surveys used to understand broader patterns around mental health, disclosure, support, and trust across pilots and ATCs globally.
02
3
major behavioral findings emerged
Pilot Interviews
13 semi-structured interviews
In-depth conversations with commercial pilots exploring lived experiences, perceived risks, and reactions to AI-mediated support using vignette-based scenarios.
03
5
cross-validated insights
Thematic Synthesis
Cross-method triangulation
Findings triangulated across all three methods to identify consistent patterns and develop trust boundaries, disclosure conditions, and AI design principles.
Human Insights
What pilots actually told us
Insight 01
Visibility shapes trust
Pilots were more willing to consider support when they understood exactly who could access their information and how it would be used. Opacity is a dealbreaker.
Data control · Regulatory transparency
It's not the tool I distrust. It's who controls the data behind it.
Insight 02
AI felt safer as a thinking space, not a diagnostic
Pilots responded positively to AI as a private, self-guided reflection tool. The moment AI felt evaluative or diagnostic, trust collapsed.
Tool positioning · Reflection vs. diagnosis
I'd use it to work things out before I talk to someone. Not as a replacement.
Insight 03
Disclosure is a systems calculation
Help-seeking was shaped by perceived consequences across medical, organizational, and professional systems — not just personal reluctance.
Regulatory context · Behavioral barriers
It's not that pilots don't want help. It's that the system makes you think twice about asking.
Insight 04
Trust depends on control
Participants consistently returned to the same need: control over data, timing, documentation, and whether — and how — anything was escalated.
User agency · Consent architecture
Give me the ability to close it and know nothing happened. That's when I'd trust it.
Data Moments
What 6,000+ responses revealed
98%
say mental health is a concern within the industry
Near-universal acknowledgment of the problem
From 6,000+ global aviation survey responses
76%
don't trust regulator mental health policies (FAA, EASA)
System-level distrust, not individual reluctance
From 6,000+ global aviation survey responses
63%
wanted to — but felt they couldn't — seek support
The 'help gap': awareness without access
From 6,000+ global aviation survey responses
13%
actually use peer support and union-based resources
Formal pathways nearly abandoned by pilots
From 6,000+ global aviation survey responses
The Framework
Trust-Bounded AI Framework
The research led to a framework showing where AI support may be acceptable within aviation mental health contexts. AI is most trusted when it sits between operational pressure and private reflection — not between the pilot and formal regulatory systems.
Trust-Bounded AI Framework
Where AI fits — and where it doesn't
AI-mediated support is most trusted when positioned at the boundary between operational pressure and private reflection — away from regulatory visibility and formal documentation systems.
Regulatory Systems
FAA · AME · Certification
Operational & Performance Demands
Airline · Colleagues · Union
Personal Reflection
Private · Self-directed · Low-stakes
AI Support Tool (CRMSON)
Advisory · Reflective · User-controlled
Hover the diagram or list items to explore each layer
Design Principles
What this means for AI mental health tools
01
Privacy must be visible and controllable
Users need to know what is stored, shared, documented, or escalated — in plain language, before they engage.
02
AI should support reflection, not diagnosis
The system should help pilots understand their state without acting as a formal evaluator. Advisory, not authoritative.
03
Disclosure pathways must feel safe
Support tools should reduce — not increase — fear of unintended visibility or career consequences.
04
Trust is shaped by the system around the tool
Adoption depends on policy, organizational context, and perceived control — not just interface design.
Influence on CRMSON
From research to product direction
These findings informed the design direction for CRMSON, an AI-enabled mental health platform for pilots, by defining trust boundaries around confidentiality, disclosure, privacy, and AI's role as a reflective support tool rather than an authority.
Responsible AI guardrails rooted in pilot trust research
Disclosure boundaries defined by certification risk context
Privacy architecture designed around user control
Safe support principles grounded in vignette interview data
Before research
AI mental health support framed primarily as access to help — solving the access problem.
After research
AI support reframed around trust, visibility, and disclosure control — solving the system-level trust problem.
Involo CRMSON
AI-enabled pilot mental health platform
Published Research Contribution
CHI 2026
ACM Conference on Human Factors in Computing Systems
“Concerns about certification and career consequences often discourage aviation professionals from seeking mental health support.” The paper examines how trust, regulatory risk, and professional culture shape mental health disclosure decisions in aviation — and the conditions under which AI-mediated support may be acceptable.
ACM CHI 2026 · Chawla et al.
Navigating Mental Health Support in Regulated Systems: Pilot Perceptions of AI-Mediated Intervention
This work investigates how aviation professionals perceive AI-mediated mental health tools through the lens of regulatory risk, certification consequences, and institutional trust — presenting a trust-bounded framework for AI support design in high-stakes occupational contexts.
University of Washington · Involo CRMSON · University of Melbourne
Reflection
What I learned
This project taught me that trust in AI is not only a product problem. In high-stakes systems, trust is shaped by policy, documentation, professional identity, and the consequences users imagine before they ever touch the interface.
For AI to support pilots meaningfully, it must be designed around control, confidentiality, and the realities of regulated work — not just around usability or accessibility.
Final takeaway
Trust is not a feature.
It is the condition that determines whether support is used at all.