A Review of User Interface Design for Interactive Machine Learning
Open accessAbstract
Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.
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In complex co-creation, AI agency patterns are typically co-operative or semi-active, often involving shared or dynamically negotiated locus of control.
“AI agency patterns are typically co-operative or semi-active [66, 191], often involving a shared or dynamically negotiated locus of control [8, 47, 109].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [47]
Section 2.1 describes interactive machine learning as a human–computer partnership in which both sides respond to input and the user drives the process. It does not establish that AI agency in complex co-creation is typically “co-operative or semi-active,” or that control is dynamically negotiated. The indexed passages provide section labels but no page numbers. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIn the textual pathway, agency distribution often involves a shared locus of control, managed through dynamic allocation negotiated via interaction, with varying granularity depending on task.
“Agency distribution often involves a shared locus of control, potentially managed through dynamic allocation negotiated via interaction [47, 68], with varying granularity depending on the task [208].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [47]
Section 2.1 says the user directs the system’s high-level behaviour while the computer can make independent decisions; section 4.2 describes a dynamic, interactive training dialogue. These passages support shared control in interactive machine learning, but they do not establish that control is dynamically allocated through negotiation. The task-dependent granularity clause is separately cited to [208], not this source. (LLM, conf=0.80)
Experimental — accuracy not yet measuredNon-expert analysts benefit from interacting with machine learning models and their data.
“Prior work has shown that non-expert analysts benefit from interacting with machine learning models and their data [1, 19, 59].”
From Interactive Model Cards: A Human-Centered Approach to Model Documentation (2022) · cites this paper as [19]
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Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
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- Authors
- Dudley, J. J., & Kristensson, P. O.
- Affiliations
- University of Cambridge (GB)
- Venue
- ACM Transactions on Interactive Intelligent Systems
- Published
- 2018-06-13
- Volume
- 8
- Issue
- 2
- Pages
- 1-37
- ISSN
- 2160-6455 (ISSN-L)
- 2160-6463
- OpenAlex
- W2794079986
- Semantic Scholar
- ad906fb0dc38f0e7a59f48c968647bd81ed778bb
- Cited by
- 343
- References
- 116
- Citations by year
- 2026: 32 · 2025: 54 · 2024: 55 · 2023: 47 · 2022: 48 · 2021: 45 · 2020: 42 · 2019: 19 · 2018: 1
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- Locations
- ACM Transactions on Interactive Intelligent Systems (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo
- Funders
- Google Faculty research award and EPSRC (EP/N010558/1 and EP/N014278/1)
- Trimble Fund
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Keywords
- interactive machine learning, user interface design, human-computer interaction, non-expert users, human-machine interaction, interface design principles, data-driven applications
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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