Heat work: choreography as an ecological enquiry of machine learning and extractivism

Bhowmik, Samir, Nguyen, Minh Anh, Touliatou, Lydia, Powell, Alison ORCID: 0000-0001-5780-9132 and Rajan, Vishnu Vardhani (2025) Heat work: choreography as an ecological enquiry of machine learning and extractivism. International Journal of Performance Arts and Digital Media. ISSN 1479-4713

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Abstract

Artificial Intelligence (AI) has the potential to both disrupt and transform the performing arts. However, the use of Machine Learning (ML) algorithms in choreography risks leading to a reductive and opaque formalization of choreographic decisions, often disregarding energy consumption and environmental impact. This article explores choreography as a method for examining the energetic materialities of ML and extractivism. Through Heat Work, a performance project, it investigates the energy and heat generated by movement data derived from tribal dances within an extraction zone. The methodology includes motion capture, data classification, and the application of a supervised ML model, with a focus on implementing a sustainable approach to choreography with AI. By examining the project’s ecological dependencies for choreographic decision-making, the article proposes a sustainable and ethical avenue of research at the intersection of AI, extractivism and performance.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	Media and Communications
Subjects:	Q Science > Q Science (General) G Geography. Anthropology. Recreation > GV Recreation Leisure
Date Deposited:	31 Mar 2025 11:18
Last Modified:	28 Apr 2025 18:45
URI:	http://eprints.lse.ac.uk/id/eprint/127738

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