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Goldsmiths - University of London

Interactive Machine Learning for End-User Innovation

Bernardo, Francisco; Zbyszynski, Michael; Fiebrink, Rebecca and Grierson, Mick. 2016. 'Interactive Machine Learning for End-User Innovation'. In: Designing the User Experience of Machine Learning Systems. Palo Alto, California, United States 27-29 March 2017. [Conference or Workshop Item] (Forthcoming)

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Abstract or Description

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 user-centred 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.

Item Type: Conference or Workshop Item (Paper)

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Departments, Centres and Research Units:

Computing
Computing > Embodied AudioVisual Interaction Group (EAVI)

Dates:

DateEvent
31 January 2017Completed
17 December 2016Accepted

Event Location:

Palo Alto, California, United States

Date range:

27-29 March 2017

Item ID:

19767

Date Deposited:

07 Mar 2017 14:39

Last Modified:

20 Jun 2017 10:27

URI: http://research.gold.ac.uk/id/eprint/19767

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