We build AI that reaches back on human terms — adaptive, interpretable, accountable for how it arrived at what it says.
We build AI that models how people learn, decide and trust — adaptive systems that show their reasoning instead of asserting it.
The MINN HAI Lab explores the synergy between the two — developing intelligent systems that are adaptive, interpretable and socially responsible, and that align with human values, cognitive processes and real-world needs.
Adaptive, interpretable, socially responsible systems — designed to perform and to be accountable for how they performed.
Enter the labKnowledge tracing: estimating what a learner has mastered, attempt by attempt, and reporting how uncertain that estimate is. The estimate is the model's claim about a person, so it is always shown with its band.
Bayesian networks and Markov blankets: identifying the smallest set of variables that renders a target conditionally independent of everything else, so an explanation names variables rather than gesturing at importance scores.
We would like to thank the following sponsors and funding agencies for supporting our research.