Karen Colbert is an educator, data scientist, TEDx speaker, and award-winning author whose work explores the intersection of artificial intelligence, education, and culture. Through research, storytelling, and systems thinking, she challenges audiences to examine the invisible assumptions embedded within technology and the institutions that shape how we learn, lead, and belong.
She is the author of The Algorithm Wasn't Built for Us, which explores how AI reflects and amplifies the assumptions already embedded in our educational systems. Her work offers a new lens for understanding intelligence, learning, and innovation by bringing together artificial intelligence, statistics, Indigenous knowledge systems, and human-centered systems design.
Karen's work has been recognized internationally, including receiving the 2025 Legendary Women of Impact Award in Paris. She has delivered keynote presentations for leading educational and AI organizations, including the Association for the Advancement of Artificial Intelligence (AAAI) Spring Symposium at Stanford University, and has been an invited speaker for the Center for Teaching Excellence and Innovation at Johns Hopkins University. She has also been featured as a guest expert on the University of Pittsburgh's Explainable AI Podcast.
She is a Research Fellow with the DRUM Circle at the American Indian College Fund, where her research explores how Indigenous knowledge systems can inform the future of teaching, learning, mathematics, and artificial intelligence. She was a founding member of the American Indian College Fund Faculty Advisory Committee and the first Data Carpentry Instructor Trainer among Tribal Colleges.
Known for making invisible systems visible, Karen helps audiences uncover the hidden assumptions shaping technology before those assumptions become tomorrow's infrastructure. Her work challenges one of the defining questions of the AI era: not what AI can do, but what AI is learning from us. She invites educators, researchers, and leaders to rethink the systems we inherit before we automate them.
