PhD Researcher · Royal Holloway, University of London

Building technology that leaves no population invisible.

When digital healthcare algorithms and self-tracking platforms are designed around standardized defaults, marginalized communities face amplified health disparities. I bridge Intersectionality Theory with empirical data systems to ensure health technologies serve, include, and dignify all populations.

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The Urgency

Why Inclusive Technology Saves Lives

Algorithmic bias and rigid design frameworks create systemic harms in real clinical workflows.

> 50%

Reduction in Care Referrals

Flawed health-risk algorithms historically cut referrals for complex care management to Black patients by more than half relative to actual medical need.

Compounded

Multi-Axis Erasure

Single-axis equity audits fail individuals at the intersections of ethnic marginalization, gender diversity, and socio-economic deprivation.

Behavioral

Emotional Dissonance

Automated biometric and affect trackers that enforce prescriptive "healthy" behaviors without cultural context trigger cognitive distress and disengagement.

Research Philosophy

Translating sociological critique into inclusive technologies.

My academic agenda at Royal Holloway, University of London, is dedicated to an unwavering objective: championing total population inclusivity across emerging technological architectures.

By integrating Kimberlé Crenshaw’s Intersectionality Theory with empirical behavioral analysis and machine learning auditing, my research interrogates how AI-driven self-tracking and diagnostic systems impact diverse user groups. My goal is to equip institutions and technologists with frameworks that replace exclusionary assumptions with equitable design.

RHUL Research Portal ↗ LinkedIn Profile ↗

Methodological Focus

Areas of Action

Connecting qualitative sociology with data science and personal informatics.

Intersectional Bias Audits

Formulating multi-attribute algorithmic evaluations that reveal how compounding demographic traits alter diagnostic and predictive models.

Self-Tracking & Chronic Care

Evaluating how automated personal health tracking, biofeedback loops, and affect indicators influence diverse patients managing chronic conditions.

Responsible Governance

Designing actionable impact assessment protocols for medical trusts and software builders deploying automated intelligence tools.

Scholarship

List of Publications & Proceedings

Refereed conferences & working manuscripts

Accepted / In Press British Academy of Management (BAM) · May 2026

You are Unhappy! AI-Driven Self-Tracked Emotional State On (Un)Healthy Food Consumption: An Emotion Dissonance Perspective

Carneiro, T., Yuen, T. W., & Ameen, N.

Investigates how algorithmic emotional classification feeds behavioral feedback loops, analyzing user dissonance when personal informatics tools dictate affective states.

Conference Contribution · Peer-Reviewed Request Pre-Print →

Working Paper / Under Revision European Conference on Information Systems (ECIS)

Designing Equitable AI-Self-Tracking Technologies for Chronic Disease Management

Carneiro, T., Yuen, T. W., & Ameen, N.

Explores participatory and intersectional interface design for chronic disease self-monitoring, identifying systemic exclusion points in automated clinical alerts.

Conference Contribution · Working Manuscript Request Working Draft →

Opportunities

Avenues for Collaboration

Fostering partnerships between research teams, health organizations, and academic institutions.

Postdoctoral & Academic Roles

Open to joint fellowship proposals, interdisciplinary grant writing, and university research appointments focused on health equity.

HealthTech Auditing & Advisory

Advising development teams on intersectional impact assessments, representative dataset curation, and ethical compliance.

Speaking & Panels

Delivering evidence-based keynotes on the sociological impacts of automated healthcare intelligence and equitable personal informatics.