Title
Interactive machine teaching: a human-centered approach to building machine-learned models
Abstract
Modern systems can augment people's capabilities by using machine-learned models to surface intelligent behaviors. Unfortunately, building these models remains challenging and beyond the reach of non-machine learning experts. We describe interactive machine teaching (IMT) and its potential to simplify the creation of machine-learned models. One of the key characteristics of IMT is its iterative process in which the human-in-the-loop takes the role of a teacher teaching a machine how to perform a task. We explore alternative learning theories as potential theoretical foundations for IMT, the intrinsic human capabilities related to teaching, and how IMT systems might leverage them. We argue that IMT processes that enable people to leverage these capabilities have a variety of benefits, including making machine learning methods accessible to subject-matter experts and the creation of semantic and debuggable machine learning (ML) models. We present an integrated teaching environment (ITE) that embodies principles from IMT, and use it as a design probe to observe how non-ML experts do IMT and as the basis of a system that helps us study how to guide teachers. We explore and highlight the benefits and challenges of IMT systems. We conclude by outlining six research challenges to advance the field of IMT.
Year
DOI
Venue
2020
10.1080/07370024.2020.1734931
HUMAN-COMPUTER INTERACTION
Keywords
DocType
Volume
Intelligent UI,user programming,interaction,industrial design,interactive machine learning
Journal
35.0
Issue
ISSN
Citations 
5-6
0737-0024
1
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Gonzalo Ramos11024.75
Christopher Meek262.77
Patrice Y. Simard31112155.00
Jina Suh417810.04
Soroush Ghorashi5123.23