AI’s Blindspots (Part 3 of 3): Toward Fair AI
- CROSS Global Research & Strategy,LLC
- Aug 2
- 3 min read
Updated: Aug 14
Dr. Shakira J. Grant
August 2, 2025
Key Takeaways:
Equitable AI demands consideration of the 3 D's: Data, Design, and Deployment.
Equity and trust are inextricably linked, requiring transparency in how AI makes decisions and when it's applied.
Developing fair, trustworthy, and equitable AI necessitates continuous monitoring and active collaboration across all stakeholders.
In Part 1 of this series, we uncovered the "Data Dilemma," revealing how non-diverse and biased datasets fundamentally undermine AI systems. Part 2, "Unequal Outcomes," then demonstrated the real-world impact of missed diagnoses in marginalized groups on inequitable resource allocation. The challenge is clear: if left unchecked, AI risks amplifying existing health disparities. So, what is our path forward? How do we build AI that is not just powerful, but truly fair and equitable?
Strategies for a More Equitable AI Future
Ensuring equity in health AI tools demands a continuous commitment from all stakeholders throughout the product lifecycle. This means understanding how these tools address or fail to address disparities in health and access to care, and critically, who benefits. Achieving equity in health AI can be effectively navigated through the 3 D’s: Data, Design, and Deployment.
Prioritize Data Diversity and Curation: Data forms the bedrock of AI tools; thus, it is the essential foundation for health AI equity. We must ensure diverse representation in datasets, encompassing factors such as age, race, sex, gender, disability status, socioeconomic status, and geographic location. Failing to do so risks perpetuating biases and inflicting direct patient harm. Just as non-diverse clinical trials can lead to erroneous assumptions about unrepresented populations, biased data inevitably results in flawed AI. To create truly patient-centered tools, we must develop them in direct collaboration with communities, addressing their unique challenges and incorporating their perspectives. This inclusive approach fosters direct feedback and builds vital trust in AI and the broader healthcare system.
Integrate Equity into AI Design and Development: Fairness must be a fundamental aspect of algorithm design, extending beyond merely the data used. Developers have a critical responsibility to rigorously monitor systems for biases, ensuring tools perform consistently across all demographic groups. Regular audits of algorithms for biases are non-negotiable, both before and after deployment. This process necessitates developing standardized metrics to track ethical and equitable usage, coupled with clear steps for remediation if these benchmarks are not met.
Ensure Transparent Deployment and Accountability: With nearly 65% of Americans reporting low trust in AI for healthcare, transparency is not merely important—it's essential. This involves not only explaining the reasoning behind AI decisions but also clearly informing patients when AI is being utilized in their care. Healthcare systems should consider implementing disclosures, similar to privacy notices, that clearly outline how AI is integrated into their services. While it may be impractical to identify every instance of AI usage, transparently communicating its primary applications builds crucial trust with patients.
Foster Continuous Learning and Adaptation: The healthcare landscape is inherently complex and ever-changing, and health AI tools must mirror this adaptability. Continuous monitoring and refinement, guided by ongoing feedback from users, clinicians, other healthcare professionals, patients, and their families, are essential. This proactive feedback loop ensures that potential issues are addressed promptly, especially for those with the greatest potential for experiencing healthcare harm.
AI-generated image showing a team collaborating for equitable AI in health. Diverse teams are essential for designing, developing, and deploying AI that serves everyone.
A Collaborative Path Forward
Creating fair health AI systems demands the collective effort of every member within the AI ecosystem. Technology developers, biopharma and biotech innovators, clinicians, healthcare systems, policymakers, and communities must actively work together to establish robust ethical guidelines, champion inclusive data practices, and demand unwavering transparency and accountability. By intentionally designing, implementing, and refining AI with equity as a fundamental principle, we can truly harness its transformative potential to improve health outcomes for everyone.
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