AI Healthcare Risk Framework Research Volunteer–Bias, Equity, Governance, Validation & Trust Failures (Remote)

About the Initiative

The BRITE Institute is expanding a structured AI Safety and Risk Framework initiative focused on identifying, analyzing, and documenting AI failure modes in healthcare and other high-risk environments.

As artificial intelligence becomes more integrated into healthcare decision-making, clinical workflows, diagnostics, patient monitoring, administrative systems, and institutional operations, new risks are emerging beyond technical performance alone.

AI systems may:

• Produce inequitable outcomes across different patient populations
• Fail regulatory, validation, or governance expectations
• Lose trust among clinicians, patients, institutions, or the public
• Be misused, underused, or inconsistently adopted in real-world settings
• Create patient safety, compliance, operational, or reputational risks

This initiative is building a scalable AI safety research and intelligence framework to help identify, analyze, and mitigate these risks before they create harm.

Volunteer Roles Available

We are seeking contributors from a wide range of backgrounds, including:

• Public health
• Health equity
• Clinical research
• Healthcare operations
• Regulatory affairs
• Healthcare compliance
• AI governance
• Ethics and policy
• Data analytics
• Social science research
• Health informatics
• Patient safety
• Quality improvement
• Life sciences
• Legal or policy research
• Clinical care
• Behavioral science
• Implementation science
• User experience or observational research

This is a fully remote and unpaid volunteer opportunity.

What You Will Do

Contributors may assist with:

• Identifying AI failure modes across bias, governance, validation, and trust categories
• Reviewing healthcare AI use cases and deployment scenarios
• Analyzing how AI failures may affect patients, clinicians, institutions, and underserved populations
• Evaluating risks related to fairness, equity, oversight, documentation, and adoption
• Identifying real-world workflow, compliance, and trust issues
• Reviewing research, reports, guidance, and healthcare AI examples
• Supporting structured failure mode documentation
• Contributing to mitigation strategy development
• Helping translate complex risks into clear, usable research outputs
• Supporting publication-oriented research deliverables
• Entering finalized findings into standardized project systems

Ideal Candidate Profile

We are looking for contributors who are:

• Detail-oriented
• Analytical
• Highly organized
• Reliable and responsive
• Comfortable with research-intensive work
• Able to follow structured workflows
• Able to work independently
• Comfortable receiving structured feedback
• Interested in responsible AI deployment
• Able to think critically about healthcare systems
• Able to connect technical, clinical, operational, equity, and governance risks
• Capable of documenting findings clearly and consistently

Strong Candidates May Have Experience or Interest In

• Health equity
• Public health
• Clinical research
• Regulatory affairs
• AI governance
• Healthcare compliance
• Patient safety
• Quality improvement
• Implementation science
• Ethics or policy research
• Healthcare operations
• Social determinants of health
• Data quality
• Healthcare technology adoption
• Clinical workflow analysis
• Observational research
• Risk management

Prior AI experience is helpful but not required.

The strongest candidates will be able to think critically about how AI systems may fail in real-world healthcare environments and how those failures may affect patients, clinicians, institutions, and vulnerable populations.

Important Notes

This initiative is fast-moving, systems-oriented, and research intensive.

Contributors should be comfortable with:

• Learning new workflows quickly
• Operating within standardized systems
• Receiving structured feedback
• Working independently
• Meeting deadlines
• Contributing consistently
• Using collaborative digital tools and remote workflows
• Maintaining accuracy across shared research systems

Remote collaboration may include:

• Slack
• Zoom
• Google Meet
• Google Docs
• Shared spreadsheets
• Research templates
• Structured data entry systems

Because this work may contribute to future publications, policy frameworks, healthcare AI safety guidance, and advanced research initiatives, the following are extremely important:

• Professionalism
• Reliability
• Confidentiality
• Operational awareness
• Attention to detail
• Consistent communication

What You’ll Gain

Volunteers may gain:

• Exposure to emerging AI safety research
• Experience contributing to a structured healthcare AI risk framework
• Publication-oriented collaboration
• Experience analyzing bias, governance, validation, and trust risks
• Interdisciplinary experience across healthcare, AI, policy, and operations
• Systems-thinking experience in responsible AI deployment
• Hands-on involvement in one of the fastest-growing areas of healthcare innovation
• Opportunities to help shape safer, more equitable, and more trustworthy AI systems

This is an opportunity to help identify the human, institutional, regulatory, and equity-related risks that determine whether AI systems are safely adopted in healthcare environments.

Additional Information

• Estimated commitment: approximately 8–15 hours per week
• Work may include structured research, documentation, and standardized data entry
• This is a merit-based and experienced-based volunteer opportunity.
• Applicants should be prepared to submit relevant work samples, research examples, writing samples, policy work, data projects, healthcare experience, regulatory experience, or related professional experience
• Selected candidates may also be asked to complete a short skills-based assessment aligned with the responsibilities outlined in this position description

Remote • Volunteer • Unpaid • Flexible Hours