TY - GEN
T1 - The UX of Interactive Machine Learning
AU - Ghajargar, Maliheh
AU - Persson, Jan
AU - Bardzell, Jeffrey
AU - Holmberg, Lars
AU - Tegen, Agnes
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/10/25
Y1 - 2020/10/25
N2 - Machine Learning (ML) has been a prominent area of research within Artificial Intelligence (AI). ML uses mathematical models to recognize patterns in large and complex data sets to aid decision making in different application areas, such as image and speech recognition, consumer recommendations, fraud detection and more. ML systems typically go through a training period in which the system encounters and learns about the data; further, this training often requires some degree of human intervention. Interactive machine learning (IML) refers to ML applications that depend on continuous user interaction. From an HCI perspective, how humans interact with and experience ML models in training is the main focus of this workshop proposal. In this workshop we focus on the user experience (UX) of Interactive Machine Learning, a topic with implications not only for usability but also for the long-term success of the IML systems themselves.
AB - Machine Learning (ML) has been a prominent area of research within Artificial Intelligence (AI). ML uses mathematical models to recognize patterns in large and complex data sets to aid decision making in different application areas, such as image and speech recognition, consumer recommendations, fraud detection and more. ML systems typically go through a training period in which the system encounters and learns about the data; further, this training often requires some degree of human intervention. Interactive machine learning (IML) refers to ML applications that depend on continuous user interaction. From an HCI perspective, how humans interact with and experience ML models in training is the main focus of this workshop proposal. In this workshop we focus on the user experience (UX) of Interactive Machine Learning, a topic with implications not only for usability but also for the long-term success of the IML systems themselves.
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U2 - 10.1145/3419249.3421236
DO - 10.1145/3419249.3421236
M3 - Conference contribution
AN - SCOPUS:85123040796
T3 - ACM International Conference Proceeding Series
BT - NordiCHI 2020 - Proceedings of the 11th Nordic Conference on Human-Computer Interaction
PB - Association for Computing Machinery
T2 - 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020
Y2 - 25 October 2020 through 29 October 2020
ER -