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Introduction to the Special Issue on Human-Centered Machine Learning
Machine learning is one of the most important and successful techniques in contemporary computer science. Although it can be applied to myriad problems of human interest, research in machine learning is often framed in an impersonal way, as merely ...
A Review of User Interface Design for Interactive Machine Learning
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 ...
Using Machine Learning to Support Qualitative Coding in Social Science: Shifting the Focus to Ambiguity
Machine learning (ML) has become increasingly influential to human society, yet the primary advancements and applications of ML are driven by research in only a few computational disciplines. Even applications that affect or analyze human behaviors and ...
Predicting User Confidence During Visual Decision Making
People are not infallible consistent “oracles”: their confidence in decision-making may vary significantly between tasks and over time. We have previously reported the benefits of using an interface and algorithms that explicitly captured and exploited ...
Crowdsourcing Ground Truth for Medical Relation Extraction
Cognitive computing systems require human labeled data for evaluation and often for training. The standard practice used in gathering this data minimizes disagreement between annotators, and we have found this results in data that fails to account for ...
Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis: Reflections on Communicating Machine Learning in Practice
- Cecily Morrison,
- Kit Huckvale,
- Bob Corish,
- Richard Banks,
- Martin Grayson,
- Jonas Dorn,
- Abigail Sellen,
- Sân Lindley
Sophisticated ubiquitous sensing systems are being used to measure motor ability in clinical settings. Intended to augment clinical decision-making, the interpretability of the machine-learning measurements underneath becomes critical to their use. We ...
A Human-in-the-Loop System for Sound Event Detection and Annotation
Labeling of audio events is essential for many tasks. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. In cases where there is very little labeled data (e.g., a single labeled example), it is often ...
Evaluation and Refinement of Clustered Search Results with the Crowd
When searching on the web or in an app, results are often returned as lists of hundreds to thousands of items, making it difficult for users to understand or navigate the space of results. Research has demonstrated that using clustering to partition ...
Observation-Level and Parametric Interaction for High-Dimensional Data Analysis
- Jessica Zeitz Self,
- Michelle Dowling,
- John Wenskovitch,
- Ian Crandell,
- Ming Wang,
- Leanna House,
- Scotland Leman,
- Chris North
Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multidimensional scaling, support data exploration by projecting datasets to two dimensions for visualization. These projections can be explored through ...
Motion-Sound Mapping through Interaction: An Approach to User-Centered Design of Auditory Feedback Using Machine Learning
Technologies for sensing movement are expanding toward everyday use in virtual reality, gaming, and artistic practices. In this context, there is a need for methodologies to help designers and users create meaningful movement experiences. This article ...