Health Data Gaps

The Challenge

AI diagnostics under-serve women and minorities, widening treatment inequalities and health disparities. The lack of diverse and representative health data leads to biased AI systems that can exacerbate existing healthcare inequities and create new forms of discrimination in medical treatment. Key concerns include:

• Biased Training Data: Medical AI systems are often trained on datasets that underrepresent women, minorities, and other marginalized groups, leading to less accurate diagnoses and treatment recommendations for these populations.
• Unequal Research Focus: Medical research and technology development often prioritize conditions that affect dominant populations, neglecting diseases and health issues that disproportionately impact underserved communities.
• Data Privacy Concerns: Efforts to collect more diverse health data must navigate complex privacy considerations, especially for communities with historical reasons to distrust medical institutions and data collection.
• Access to AI Healthcare: Advanced AI diagnostic tools and treatments may be less accessible to underserved communities due to cost, geographic location, or technological barriers, further widening health disparities.

Our Approach

The Global Tech Governance Institute takes an equity-focused approach to addressing health data gaps:

• Inclusive Data Collection Standards: Developing guidelines and requirements for representative and diverse health data collection to ensure AI systems are trained on datasets that reflect the full spectrum of human diversity.
• Equity Impact Assessments: Implementing mandatory evaluations of how health AI systems perform across different demographic groups before deployment, with requirements to address disparities in performance.
• Community-Led Data Governance: Empowering communities to participate in decisions about how their health data is collected, used, and protected, building trust and ensuring ethical data practices.
• Equitable Access Policies: Creating frameworks to ensure that AI-driven healthcare innovations are accessible to all communities, regardless of socioeconomic status or geographic location.

Current Initiatives

Our work in this area currently includes:

Matrix Integration

Key Publications