The Prediction Machine is taking place as part of an Impact Research Fellowship (funded by the EPSRC Impact Acceleration Account) and a broader research project – ‘The Performing Data Platform’ – at the Mixed Reality Lab / Horizon Research Institute, University of Nottingham.
Further information about this research for scientists, policy makers and NGOs is available in this pdf document
Jacobs R. Giannachi G. Benford S. Blum J., Shipp V., Flintham M., Data Dialogues: Critical Reflections on HCI and Socially Engaged Arts Practice, Workshop Position Paper Socially Engaged Art, CHI 2014
Jacobs, R., Benford, S., Giannachi, G., Blum J., Shipp V., (2014, April). A Conversation Between Trees: What data feels like in the forest. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 129-138). ACM. (Winner of Best Paper Honorary Mention)
Jesse Blum, Martin Flintham, Derek McAuley, Rachel Jacobs, Matt Watkins, Rebecca Lee, Robin Shackford, Mark Selby, Silvia Leal and Gabriella Giannachi, Timestreams: Supporting Community Engagement in the Climate Change Debate, Digital Futures Conference, Aberdeen, 2012 (Best Regular Paper)
Jacobs R. Giannachi G. Benford S. Greenman A. Smoke and Mirrors: Liveness, Presence and Suspension of Disbelief, Workshop Position Paper Exploring HCI’s Relationship with Liveness CHI 2012
Jacobs R. Selby M. Benford S. Engaging With Slowness: A Temporal Experience of Climate Change, Workshop Position Paper Slow Technology DIS 2012
Jacobs R. Giannachi G. Benford S. Performing Natures’ Footprint, VISUAL AND PERFORMING ARTS, Edited by Stephen Andrew Arbury Aikaterini Georgoulia, Athens Institute for Education and Research 2011
It is notoriously difficult but also increasingly vital to engage the public with complex scientific issues, and especially scientific climate data. Previous research has established the interdisciplinary foundations for understanding how artists can create a deep emotional engagement with data, walking the line between meaningful and authentic scientific engagement, to foster dialogue across scientists, artists, technologists and the public.
This has the potential to increase impact for the science community and provide a new way to stimulate and shape debate around the major science issues of our times such as climate change. As such it can have a major impact on scientists, science communicators (including festivals and venues), policy makers, technologists designing tools for artists and scientists, the public through targeted community groups and participation in exhibitions and demonstrations, policy makers and of course, the artists themselves.
This impact Research taking place at the Mixed Reality Lab/Horizon, University of Nottingham in collaboration with Dr Candice Howarth (Anglia Ruskin University) and Dr Carlo Buontempo (Hadley Centre, MET Offie UK) involves research into the role of artists in climate science communication and study the impact of this work on these communities. Using ‘The Prediction Machine’ as a demonstration project to specifically investigate how to engage local communities with climate forecasting.
This research seeks to answer the following questions: How can we enable non-experts, communities and individuals to recognize and respond to the impact of climate change in their everyday lives through artistic engagement? What is the impact on people participating in the exhibitions, workshops and demonstrations of ‘The Prediction Machine’ and what is the impact of this work on the science community?
Further to this, the research seeks to investigate the following questions for the Digital Economy: Can tools and systems support the marking and capturing of climate change within our everyday lives? Can accessible tools be developed to support non-experts, communities and individuals to act in response to their everyday/personal experiences of climate change?
In support of these question, a series of HCI (Human Computer Interaction) tools have been embedded within the artwork in order to understand;
(a) The ways in which self-selecting participants understand and engage with data (climate and weather)
(b) How self-reported and observed factors influence that understanding
(c) The role of the artist in supporting that understanding
(d) Points of decision that influence the ultimate instantiation of climate and weather data in the Prediction Machine
(e) Public response to that artistic interpretation, including intended action
(f) The extent to which the technologically-mediated artistic process can be modeled for replication (e.g. what models can help us to better understand what is going on so that others can do the same)
One of the key challenges is the way users of the machine are invited to embody data through their physical interaction with the machine. The hand crank acts as a demonstration of the energy required to power the technology in our everyday lives, providing a metaphor for our ongoing impact on the environment. Yet, this reveals an interesting tension between the physical interaction and the data, exploring how aesthetic and tangible experience occur whilst bringing the data to the fore.
The participants in the workshops were also invited to observe the weather and then use this experience to write the predictions, printed out by the machine. By comparing personal, sensory observations with scientific data in this way the participants were engaged in a process of ‘performing’ and ‘interpreting’ the data as a physical, logical, emotional and sensory experience. Users of the machine were then invited to take the predictions away with them as souvenirs of the moment in time when they interacted with the machine, requiring them to reflect for a moment on the weather outside and imagine a future climate scenario based on that moment.
This process raises important issues around how to walk the line between artistic interpretations of data and scientific authenticity. By combining observed experience with scientific data this work questions how explicit to make the link between the scientific and artistic interpretations of data and how much to reveal about the algorithms which control the predictions.