research-article

Power to the People: : The Role of Humans in Interactive Machine Learning

Authors:

Saleema Amershi,

W. Bradley Knox, and

Todd KuleszaAuthors Info & Claims

AI Magazine, Volume 35, Issue 4

Pages 105 - 120

https://doi.org/10.1609/aimag.v35i4.2513

Published: 01 December 2014 Publication History

Abstract

Systems that can learn interactively from their end‐users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that demonstrate how interactivity results in a tight coupling between the system and the user, exemplify ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. After giving a glimpse of the progress that has been made thus far, we discuss some of the challenges we face in moving the field forward.

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Published In

cover image AI Magazine

AI Magazine Volume 35, Issue 4

Winter 2014

126 pages

ISSN:0738-4602

EISSN:2371-9621

DOI:10.1002/aaai.v35.4

Issue’s Table of Contents

© 2014 The Authors. AI Magazine published by John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence.

Publisher

American Association for Artificial Intelligence

United States

Publication History

Published: 01 December 2014

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Cited By

Zhu QWang DMa SWang AChen ZKhurana UMa X(2024)Towards Feature Engineering with Human and AI’s Knowledge: Understanding Data Science Practitioners’ Perceptions in Human&AI-Assisted Feature Engineering DesignProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661517(1789-1804)Online publication date: 1-Jul-2024
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Piorkowski DOstrand RBrimijoin KHe JAlbert EHoude S(2024)Towards Interactive Guidance for Writing Training Utterances for Conversational AgentsProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665553(1-15)Online publication date: 8-Jul-2024
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Muijlwijk HWillemsen MSmyth BIJsselsteijn W(2024)Benefits of Human-AI Interaction for Expert Users Interacting with Prediction Models: a Study on Marathon RunningProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645205(245-258)Online publication date: 18-Mar-2024
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Mohammadzadeh BFrançoise JGouiffès MCaramiaux B(2024)Studying Collaborative Interactive Machine Teaching in Image ClassificationProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645204(195-208)Online publication date: 18-Mar-2024
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