The tech industry is quietly amassing “Diversity Debt”—a hidden liability that arises from neglecting inclusivity, diverse data practices, and ethical governance in AI development. Like technical debt, Diversity Debt grows over time, with small biases in datasets, synthetic data generation, and model design compounding into systemic flaws that are increasingly costly and complex to address. These unchecked biases erode trust, stifle innovation, and create products that fail to serve the diverse populations they’re meant to empower.

In this keynote, Alison Cossette unpacks the concept of Diversity Debt and its far-reaching consequences, from skewed AI outcomes to diminished market opportunities. Drawing on her pioneering work in data provenance and governance with Neo4j and the FORGE platform, Alison demonstrates how organizations can identify and address compounding bias across all stages of AI development. Using compelling examples—such as synthetic datasets that reinforce disparities and feedback loops that amplify exclusion—she highlights the urgency of tackling bias before it scales.

Attendees will gain actionable insights into reducing Diversity Debt through inclusive governance frameworks, ethical data practices, and proactive model evaluation. Whether you’re a startup founder, data scientist, or industry leader, this talk will equip you to build AI systems that reflect the diversity of the world and unlock innovation without limits. Join us to discover why paying down Diversity Debt isn’t just ethical—it’s essential for creating AI that thrives in an increasingly complex, interconnected