AI Magazine· 2014Q2· Review
Power to the People: The Role of Humans in Interactive Machine Learning
- 1,030citations
- Q2SCImago
- 2014year
Short summary
Interactive machine learning systems are increasingly common, but progress is shifting from solely algorithmic advances to understanding and integrating the human user. This paper argues for a user-centric approach to improve both user experience and system effectiveness.
AI-generated from the title and abstract; the full text is not read.
Key points
- Interactive machine learning systems are rapidly becoming more prevalent.
- Progress is increasingly driven by understanding user interaction, not just machine learning algorithms.
- A user-centric approach can lead to improved system effectiveness and user experience.
- Case studies illustrate how interactivity creates a close link between system and user.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. AI Magazine, 2014 · DOI ↗
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Field: Software
SoftwareComputer Science