[PDF][PDF] Interactive machine learning for end-user innovation

F Bernardo, M Zbyszynski, R Fiebrink… - 2017 AAAI Spring …, 2017 - cdn.aaai.org
2017 AAAI Spring Symposium Series, 2017cdn.aaai.org
User interaction with intelligent systems need not be limited to interaction where pre-trained
software has intelligence “baked in.” End-user training, including interactive machine
learning (IML) approaches, can enable users to create and customise systems themselves.
We propose that the user experience of these users is worth considering. Furthermore, the
user experience of system developers—people who may train and configure both learning
algorithms and their user interfaces—also deserves attention. We additionally propose that …
Abstract
User interaction with intelligent systems need not be limited to interaction where pre-trained software has intelligence “baked in.” End-user training, including interactive machine learning (IML) approaches, can enable users to create and customise systems themselves. We propose that the user experience of these users is worth considering. Furthermore, the user experience of system developers—people who may train and configure both learning algorithms and their user interfaces—also deserves attention. We additionally propose that IML can improve user experiences by supporting usercentred design processes, and that there is a further role for user-centred design in improving interactive and classical machine learning systems. We are developing this approach and embodying it through the design of a new User Innovation Toolkit, in the context of the European Commission-funded project RAPID-MIX.
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