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Machine Teaching with Generative Models for Human Learning
Michael Doron · Hussein Mozannar · David Sontag · Juan Caicedo

Experimental scientists face an increasingly difficult challenge: while technological advances allow for the collection of larger and higher quality datasets, computational methods to better understand and make new discoveries in the data lag behind. Existing explainable AI and interpretability methods for machine learning focus on better understanding model decisions, rather than understanding the data itself. In this work, we tackle a specific task that can aid experimental scientists in the era of big data: given a large dataset of annotated samples divided into different classes, how can we best teach human researchers what is the difference between the classes? To accomplish this, we develop a new framework combining machine teaching and generative models that generates a small set of synthetic teaching examples for each class. This set will aim to contain all the information necessary to distinguish between the classes. To validate our framework, we perform a human study in which human subjects learn how to classify various datasets using a small teaching set generated by our framework as well as several subset selection algorithms. We show that while generated samples succeed in teaching humans better than chance, subset selection methods (such as k-centers or forgettable events) succeed better in this task, suggesting that real samples might be better suited than realistic generative samples. We suggest several ideas for improving human teaching using machine learning.

Author Information

Michael Doron (Broad Institute of MIT and Harvard)
Hussein Mozannar (Massachusetts Institute of Technology)
David Sontag (MIT)
Juan Caicedo (Broad Institute)

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