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Unlock AI's Full Potential: The Power of FAIR Data
Session Chair(s)
Anuj Uppal, MS
Vice President, Life Sciences Transformation
Campana & Schott, United States
AI underperforms when data isn’t machine-actionable. This talk shows how FAIR + open standards turn fragmented R&D data into fuel for LLMs and analytics—via a mini-rubric, case studies, and a 90-day roadmap to lift AI-readiness.
Learning Objective : Apply a mini-FAIR rubric to one dataset/document to diagnose AI-readiness and summarize 2–3 priority gaps; Recognize and select the appropriate standards—such as PIDs, metadata, controlled vocabularies, that best enable a target AI use case (e.g., RAG, model reuse); Recognize the key components of a 90-day plan—including roles, metrics, checkpoints—designed to elevate FAIR maturity and improve AI outcomes.
Speaker(s)
AI Readiness: A Statistical View
Kaveen Hiniduma
Ohio State University, United States
Graduate Research Associate
AI Readiness Organizational Barriers
Kathleen Rand, PHARMD
Procter & Gamble, United States
Principal Scientist
The FAIR Principals 10 Years Later
Ted Slater, MA, MS
EPAM Systems, United States
Global Head, Knowledge Engineering & Data Advisory
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