Alison Cossette
Data Science Strategist, Advocate, Educator
Burlington, Vermont, United States
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Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications. Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies. She is currently a Volunteer Member of the US Department of Commerce - National Institute of Standards and Technology's Generative AI Public Working Group Alison’s academic journey includes Masters of Science in Data Science studies, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science. Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.
Area of Expertise
Topics
Data Provenance: The Hidden Foundation for Risk Management in AI and Kubernetes Ecosystems
For many organizations, the concept of data provenance remains an untapped area of focus, yet it underpins every aspect of trust, compliance, and operational risk. As Kubernetes continues to drive innovation in scalable data platforms and AI systems, gaps in data traceability and origin verification introduce risks that many teams are unaware of until they manifest as failures, inefficiencies, or regulatory noncompliance.
This talk will uncover why data provenance is critical to modern AI and containerized systems, particularly within Kubernetes ecosystems. We’ll explore real-world scenarios where missing or unclear data origins led to compounding risks, and how understanding provenance can mitigate cascading impacts on AI models, synthetic data use, and compliance efforts.
Attendees will leave with a clear understanding of the importance of provenance in managing data and risk. We’ll discuss simple, actionable steps to introduce provenance into Kubernetes workflows, enabling teams to better track, validate, and secure their data. By highlighting practical examples and emerging trends, this session will empower participants to view provenance not as an abstract concept, but as a critical enabler of trust and resilience in their AI and data systems.
AI’s Diversity Debt: How Compounding Bias Threatens Innovation—and What We Can Do About It
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
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