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Power to the People: The Role of Humans in Interactive Machine Learning
Open accessAbstract
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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“The resulting tight coupling between humans and machines, with the two influencing one another, makes it increasingly difficult, or even impossible, to decouple their influence on the resulting model (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
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“Owing to their inherent complexity and uncertainty, augmentation efforts often fail (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
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“Contrary to automation, augmentation demands continued human involvement and experimentation (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
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“Through augmentation, humans and machines become so closely intertwined that they collectively exhibit entirely new, emergent behaviors, which neither show individually (Amershi et al., 2014).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi et al., 2014
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“Augmentation is therefore a coevolutionary process during which humans learn from machines and machines learn from humans (Amershi, Cakmak, Knox, & Kulesza, 2014;Rahwan et al., 2019).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Amershi, Cakmak, Knox, & Kulesza, 2014
In the citing paper
“-Data Design: Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges [17,23,25-27, 31,33,41,44,74-76,105,114-116].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [25]
In the citing paper
“-Visualization: Technical challenges on visualization techniques for the design of ML applications [25,56,63,122,123].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [25]
In the citing paper
“-Tools: Needs of designing tools for ML applications, such as tools for non-expert ML designers, visualization tools for understanding the relationships between data and the behavior of algorithms, tools for ensuring interoperability with other tools, and domain-specific language (DSL) support [16,25,42,66].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [25]
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- Amershi S., 2009, Proceedings of the ACM Symposium on User Interface Software and Technology, 2009 (UIST 2009), 47
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Pilault, J., Garcia, X., Bražinskas, A., & Fırat, O. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.31 · Cited by 18
Enabling people to harness and control EDM for lifelong, life-wide learning.
Kay, J. (2016) · Educational Data Mining · Cited by 5
Animated analysis of geoscientific datasets: An interactive graphical application
Morse, P. E., Reading, A. M., & Lueg, C. (2017) · Computers & Geosciences · doi:10.1016/j.cageo.2017.07.006 · Cited by 5
End-to-End Active Learning for Computer Security Experts
Beaugnon, A., Chifflier, P., & Bach, F. (2018) · HAL · Cited by 5
Semantic-Based Interaction for Teaching Robot Behavior Compositions Using Spoken Language
Paléologue, V., Martin, J., Pandey, A. K., & Chétouani, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05204-1_41 · Cited by 5
Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation
Großmann, N., Bernard, J., Sedlmair, M., & Waldner, M. (2021) · Visualization · doi:10.1109/vis49827.2021.9623326 · Cited by 5
Learning From Visual Demonstrations via Replayed Task-Contrastive Model-Agnostic Meta-Learning
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Learning From Demonstrations Via Multi-Level and Multi-Attention Domain-Adaptive Meta-Learning
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Human-Interactive Robot Learning (HIRL)
Mirsky, R., Baraka, K., Faulkner, T. K., Hart, J., Yedidsion, H., & Xiao, X. (2022) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889551 · Cited by 5
Studying Collaborative Interactive Machine Teaching in Image Classification
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Uncertainty-Based Deep Learning Networks for Limited Data Wetland User Models
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Metadata
- Authors
- Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T.
- Affiliations
- Microsoft (United States) (US)
- Massachusetts Institute of Technology (US)
- Oregon State University (US)
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2014-12
- Volume
- 35
- Issue
- 4
- Pages
- 105-120
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W1501005121
- Cited by
- 1,084
- References
- 55
- Citations by year
- 2026: 139 · 2025: 127 · 2024: 106 · 2023: 101 · 2022: 111 · 2021: 172 · 2020: 111 · 2019: 83 · 2018: 63 · 2017: 33 · 2016: 22 · 2015: 14 · 2014: 2
- FWCI
- 23.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI Magazine (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- interactive machine learning, human-in-the-loop learning, user modeling, user experience, human-computer interaction, learning systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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