Emily King, Department of Mathematics, Colorado State University
Interpretable, Explainable, and Adversarial AI: Data Science Buzzwords and You (Mathematicians)
Many state-of-the-art methods in machine learning are black boxes which do not allow humans to understand how decisions are made. In a number of applications, like medicine and atmospheric science, researchers do not trust such black boxes. Explainable AI can be thought of as attempts to open the black box of neural networks, while interpretable AI focuses on creating white boxes. Adversarial attacks are small perturbations of data, often images, that cause a neural network to misclassify the data. Such attacks are potentially very dangerous when applied to technology like self-driving cars. After a gentle introduction to these topics and data science in general, a sampling of methods from geometry, linear algebra, and harmonic analysis to attack these issues will be presented.
More information about this speaker may be found at https://www.math.colostate.edu/~king/