The climate crisis is an urgent and complex global challenge, requiring transformative action from diverse stakeholders, including governments, civil society, and grassroots movements. Conventional top-down approaches to climate governance have proven insufficient (e.g. UNFCCC, COP events), necessitating a shift towards more inclusive and polycentric models that incorporate the perspectives and needs of diverse communities (Bliznetskaya, 2023; Dorsch & Flachsland, 2017). The independent, multidisciplinary approach of citizen-led activist groups can provide new insights and redefine challenges and opportunities for climate governance and regulation. Despite their important role in developing effective climate action, these citizen-led groups often face significant barriers to decision-making participation, including structural, practical, and legal challenges (Berry et al., 2019; Colli, 2021; Marquardt et al., 2022; Tayler & Schulte, 2019).
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Over the past few years the tone of the debate around climate change has shifted from sceptical to soberingly urgent as the global community has prioritised the research into solutions which will mitigate greenhouse gas emissions. So far this research has been insufficient. One of the major problems for driving public and private stakeholders to implement existing solutions and research new ones is how we communicate about climate change (Stoknes, 2014). There seems to be a lack of common language that drives the scientific community away from policymakers and the public. Due to this lack, it is hard to translate findings into viable and sustainable solutions and to adopt new climate-neutral economies and habits.
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This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
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