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Adeeb Noor is Professor of Data Science & AI at King Abdulaziz University. He studies what happens when metrics become decisions about people, working across AI, healthcare, education and public institutions.
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Adeeb Noor is Professor of Data Science & AI at King Abdulaziz University. He studies what happens when metrics become decisions about people, working across AI, healthcare, education and public institutions.
Adeeb Noor is Professor of Data Science & AI at King Abdulaziz University. His work combines research, institutional leadership and system building around one recurring question: what happens when a score, category or ranking becomes a decision about people? His work spans decision intelligence, biomedical informatics, digital health, workforce planning and AI-enabled education. He has built and led initiatives across university, Ministry of Education and healthcare settings, and develops tools that make evidence, limits and decision consequences easier to inspect and hold accountable.
Adeeb Noor is Professor of Data Science & AI at King Abdulaziz University, a researcher and institutional leader with more than 18 years across government, higher education and healthcare. His work connects AI and biomedical-informatics research with systems and operating models used to support real institutional decisions. His public thesis is that “a score is not a decision”: an average or score can hide who changes, who sets capacity, what a category actually means, and whether an output is evidence of readiness at all. These questions appear in RIDI for ranking decisions, research on drug interactions and biomedical knowledge completeness, MIYAR for workforce architecture, and iSCARB and IMAM for AI-enabled education. Alongside research, he has held advisory and executive roles with the Ministry of Education, King Abdulaziz University and digital-health initiatives. His current focus is on making AI-supported decisions more transparent, inspectable and accountable.
Talk themes are framed around the gap between measurement and decision, with examples adapted to the audience.
What the Score Hides; When Evaluation Becomes a Decision; Knowledge Completeness Before Inference; What Counts as Evidence of Readiness in the AI Era.
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