Designing for algorithmic awareness - Materializing machine learning

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Designing for algorithmic awareness - Materializing machine learning

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Publication 1-year master student thesis
Title Designing for algorithmic awareness - Materializing machine learning
Author Thomas Sandahl, Christensen
Date 2017
English abstract
The following paper explores how to materialize machine learning in order to make it tangible and sensible thereby offering users the needed tools for engaging the technology in reflective use. The project draws inspiration from the Static! research program on designing for energy awareness. Their approach to energy as a design material is adapted to the field of machine learning in order to use their tactics to engage the problem of unpacking and materializing machine learning with the goal of enabling reflective use. The project is grounded in Spotify and their use of machine learning. In particular their Discover Weekly feature is argued for as an example of a service that relies heavily on machine learning algorithms. With inspiration from The Living Lamp the combination of algae and microcontroller is framed as a computational composite. The composite is analysed based on the material strategy. The analysis is directed towards exploring the composites suitability for materializing the qualities of machine learning. The composite was found suitable for the design problem. Subsequently the composite is engaged in a prototype centric design process aimed at using it to materialize machine learning. The end result of the process is a functional prototype named Growing Data. The design uses the algae/computer composite to grow algae in relation to the data a user's music listening activities produce, thereby becoming a local representation of the distant abstract data that feed into the service’s machine learning algorithms. It exemplifies one possible strategy for materializing machine learning.
Publisher Malmö högskola/Kultur och samhälle
Pages 43
Language eng (iso)
Subject computational composites
Machine learning
Handle http://hdl.handle.net/2043/23502 Permalink to this page
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