Abstract
After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.
Highlights
Sensors 2021, 21, 2514. https://Artificial Intelligence (AI) represents the broad spectrum of automated decision making from conditional logic to Neural Networks
Some of the reasons are the frequent application of Artificial Intelligence (AI) models in the real world, an increasing number of interactions between humans and AI, professionals raising concerns regarding the black-box nature of Deep Learning (DL), and questions about the AI models’
Even though Human-Centered Machine Learning (HCML) is a nascent field, its origin dates back decades, and the exact term itself is more than a decade old
Summary
Sensors 2021, 21, 2514. https://Artificial Intelligence (AI) represents the broad spectrum of automated decision making from conditional logic to Neural Networks. Artificial Intelligence (AI) represents the broad spectrum of automated decision making from conditional logic to Neural Networks. Decisions or predictions made using data-driven techniques fall into Machine Learning (ML), a subset of AI. The subset of Machine Learning techniques that use Deep Neural Networks (DNN) is called Deep Learning (DL) (see Figure 1).
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