A Review of User Interface Design for Interactive Machine Learning

Dudley, John J.; Kristensson, Per Ola

doi:10.1145/3185517

Cited by 237 publications

(161 citation statements)

References 99 publications

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“…Other guidelines (Horvitz, 1999;Dudley & Kristensson, 2018) paved the way for Microsoft's Guidelines for AI-Human Interaction (https://www.microsoft.com/enus/research/project/guidelines-for-human-ai-interaction/), which has 18 guidelines for initial use, normal use, coping with problems, and changes over time (Amershi et al, 2019). These guidelines emphasize user understanding and control, while addressing ways for the system to "make clear why the system did what it did" and "learn from user behavior.…”

Section: Prometheus Principles and Examplesmentioning

confidence: 99%

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

Shneiderman

2020

International Journal of Human–Computer Interaction

559

211

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Well-designed technologies that offer high levels of human control and high levels of computer automation can increase human performance, leading to wider adoption. The Human-Centered Artificial Intelligence (HCAI) framework clarifies how to (1) design for high levels of human control and high levels of computer automation so as to increase human performance, (2) understand the situations in which full human control or full computer control are necessary, and (3) avoid the dangers of excessive human control or excessive computer control. The methods of HCAI are more likely to produce designs that are Reliable, Safe & Trustworthy (RST). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.

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Section: Prometheus Principles and Examplesmentioning

confidence: 99%

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

Shneiderman

2020

International Journal of Human–Computer Interaction

559

211

View full text Add to dashboard Cite

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“…Moreover, recent reports with positive results of machine learning applications in solving the problem of interest have been observed extensively in the literature. This is because machine learning has the ability to explore complicated relationships between factors in various real-world problems [ 24 , 25 ]. For flood modeling, Nandi, et al [ 26 ] constructed a flood hazard map in Jamaica based on logistic regression and principal component analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Ngo

Hoang

Pradhan

et al. 2018

Sensors

118

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Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

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“…In contrast to AL, the sample selection in interactive machine learning (IML) is driven by the user. Dudley et al [17] describe a general approach to interface design for IML providing an overview of challenges and common guiding principles. Arendt et al [2] present an IML interface with model feedback after every interaction by updating the items shown for each class.…”

Section: Visual Active and Interactive Machine Learningmentioning

confidence: 99%

FDive: Learning Relevance Models Using Pattern-based Similarity Measures

Dennig

Polk

Lin

et al. 2019

2019 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Figure 1: FDIVE learns to distinguish relevant from irrelevant data through an iteratively improving classification model by learning the best-fitting feature descriptor and distance function. (1) Users express their notion of relevance by labeling a set of query items, in this case, images. (2) These labels are used to rank all similarity measures by their ability to distinguish relevant from irrelevant data. (3) The system applies the selected similarity measure to learn a Self-Organizing Map (SOM)-based relevance model. Users explore and refine the model by supplying relevance labels in uncertain data regions, especially near the decision boundaries. ABSTRACTThe detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDIVE, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDIVE enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.

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A Review of User Interface Design for Interactive Machine Learning

Cited by 237 publications

References 99 publications

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

FDive: Learning Relevance Models Using Pattern-based Similarity Measures

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