02-18-2026
“If we blindly trust an algorithm, we could have considered many patients ‘healthy’ when they are not,” writes Aaron Lai.
The Daniels School MBA alum’s article for INFORMS, “Health Data and AI: Biased Data, Biased Outcome,” explores an urgent question at the intersection of technology and health policy: Can we trust the model?
Drawing on decades in health care analytics, Lai highlights a critical — and often overlooked — insight: responsible AI starts with responsible data. Without clear governance and transparency in how data are captured, documented and used, even the most advanced AI models risk producing biased or misleading outcomes.
Lai calls for a new “social contract with AI,” where regulatory agencies, innovators and health care professionals align on ethical frameworks and accountability.
Lai supports the Daniels School’s Krenicki Center for Business Analytics and Machine Learning as a Senior Fellow. He is pursuing a Doctor of Technology from Purdue Polytechnic Institute, focusing on responsible AI and health data.
