AI Healthcare Risk Intelligence & Business Analysis Volunteer (Remote)

About the Initiative

The BRITE Institute is expanding a structured AI Healthcare Risk Framework initiative focused on identifying, analyzing, categorizing, 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, operational processes, and institutional governance, new risks are emerging beyond technical performance alone.

AI systems may fail because of issues related to data quality, model design, validation, deployment, workflow integration, human decision-making, monitoring, oversight, compliance, or organizational readiness.

This initiative is building a scalable AI safety research and intelligence framework to help identify, analyze, prioritize, and mitigate these risks before they create patient harm, operational disruption, financial exposure, compliance failures, or institutional trust concerns.

This volunteer role is focused on supporting the next layer of the framework: data categorization, pattern analysis, lifecycle classification, research-quality review, and business-value insight.

Role Focus

This role will support the analytical intelligence layer of the AI Healthcare Risk Framework.

It includes structured data analysis, but the primary focus is translating AI failure-mode data into risk intelligence, business-value insight, governance analysis, lifecycle classification, and framework-development support.

The goal is not only to organize data, but to help determine what the failure-mode data is signaling across healthcare AI systems, institutions, workflows, and governance environments.

Key Areas of Analysis

This role may involve analysis across several dimensions of the framework.

Failure Mode Categorization

Reviewing failure modes and help classify them.

AI Lifecycle Failure Point Classification

Supporting classification of where in the AI lifecycle the failure occurred, became visible, or should have been controlled.

Research Quality and Data Integrity Review

Helping assess whether failure-mode entries are strong, usable, and well-supported.

Business-Value and Risk Intelligence Analysis

Helping translate failure-mode data into useful insight for framework development, project leadership, and future strategic use.

What You Will Do

Contributors may assist with:

• Reviewing structured AI failure-mode data
• Categorizing failure modes across approved framework dimensions
• Identifying recurring patterns across risk, impact, lifecycle stage, and mitigation fields
• Supporting AI lifecycle failure-point classification
• Evaluating strong versus weak failure-mode examples
• Flagging incomplete, vague, duplicative, or poorly supported entries
• Reviewing whether citations and context support the failure-mode claim
• Supporting development of quality review rubrics
• Helping organize findings into clear analytical summaries
• Translating complex risk data into practical business, governance, operational, and clinical insight
• Supporting dashboard, reporting, or structured analysis concepts
• Helping preserve traceability between Failure ID, source, category, lifecycle stage, impact, and mitigation
• Supporting publication-oriented and framework-development deliverables

Ideal Candidate Profile

We are looking for contributors who are:

• Highly analytical
• Detail-oriented
• Organized and reliable
• Comfortable working with structured data
• Able to follow standardized workflows
• Able to work independently after receiving instructions
• Comfortable with ambiguity while still using objective reasoning
• Able to distinguish strong evidence from weak evidence
• Able to identify patterns without overgeneralizing
• Able to connect technical, clinical, operational, financial, and governance risks
• Interested in healthcare AI, responsible AI, risk management, or business analytics
• Able to communicate findings clearly and professionally

The strongest candidates will be able to move beyond basic data organization and identify what the data is signaling from a healthcare risk, business intelligence, governance, and operational-value perspective.

Strong Candidates May Have Experience or Interest In

• Data analysis
• Business analytics
• Healthcare analytics
• AI governance
• Responsible AI
• Risk management
• Healthcare operations
• Clinical workflow analysis
• Health informatics
• Patient safety
• Quality improvement
• Compliance or audit
• Regulatory affairs
• Financial analysis
• Process improvement
• Business intelligence
• Research evaluation
• Data visualization
• AI lifecycle management
• Machine learning or AI systems
• Consulting, strategy, or operational analysis

Prior healthcare AI experience is helpful but not required.

Prior experience with structured analysis, research review, data categorization, business intelligence, or risk analysis is strongly valuable.

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
• Meeting deadlines
• Maintaining accuracy across shared systems
• Working independently after instructions are provided
• Using collaborative digital tools and remote workflows
• Preserving confidentiality
• Following project-specific classification rules
• Avoiding unauthorized restructuring of shared data systems

Remote collaboration may include:

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

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

• Professionalism
• Reliability
• Confidentiality
• Operational awareness
• Attention to detail
• Clear communication
• Analytical discipline
• Traceability
• Accuracy
• Responsiveness

What You’ll Gain

Volunteers may gain:

• Experience supporting a structured healthcare AI risk framework
• Exposure to emerging AI safety and healthcare AI governance work
• Experience analyzing real-world AI failure modes
• Hands-on involvement in AI risk categorization and lifecycle classification
• Experience translating research data into business and operational insight
• Exposure to healthcare AI risk mitigation, governance, monitoring, and compliance concepts
• Experience supporting publication-oriented research and framework development
• Practice identifying quality gaps, weak signals, and emerging risk patterns
• Interdisciplinary experience across AI, healthcare, operations, governance, and business analytics
• A stronger understanding of how AI systems fail in real-world healthcare environments

This is an opportunity to help build the analytical intelligence layer of a healthcare AI risk framework and support safer, more reliable, more accountable AI adoption.

Additional Information

• Estimated commitment: approximately 10–20 hours per week
• This is a fully remote, unpaid volunteer opportunity
• Work may include structured data review, categorization, research-quality assessment, pattern analysis, and written analytical summaries
• This is a merit-based and experience-based volunteer opportunity
• Applicants should be prepared to submit relevant work samples, data projects, analytics examples, research examples, writing samples, business analysis work, AI projects, healthcare experience, risk analysis work, or related professional experience
• Selected candidates may be asked to complete a short skills-based assessment aligned with the responsibilities outlined in this position description

Remote • Volunteer • Unpaid • Flexible Hours