Performing algorithms: Automation and accident

© 2019 Sean Patrick Dockray"Performing Algorithms: Automation and Accident" investigates how artists might stage encounters with the algorithms driving our post-industrial, big-data-based, automatic society. Several important theories of this contemporary condition are discussed, including control societies, post-industrial societies, the automatic society, the cybernetic hypothesis, and algorithmic governmentality. These concepts are interwoven with histories of labour and automation, recent developments in machine learning and neural networks, and my own past work. Through a series of expanded lecture performances that describe our algorithmic condition while setting it into motion, this research seeks to discover ways in which to advance new critical positions within a totalizing technical apparatus whose very design preempts it. The included creative works have been performed, exhibited, and published between 2014 and 2018. They are made available online through an artificially intelligent chatbot, a frequent figure in the research, which here extends the concerns of that research through to how the work is framed and presented. The thesis focuses on both generative art and the lecture performance, which converge in performing algorithms but are generally not discussed in connection with one another. They emerged in parallel as artistic methods, however, at a time when management and computation were taking root in the workplace in the 1960s. Furthermore, as the Internet became widespread from the 1990s, generative art and the lecture performance each found renewed prominence. With human language and gesture increasingly modelling itself on the language of computation and work constantly reshaped by the innovations of capital, this project identifies “not working” both in terms of the technological breakdown and also as a condition of labour under automation. A discussion of the first fatal accident involving a self-driving vehicle illustrates this dual condition. Shifting from glitch art’s preoccupation with provoking errors to a consideration of not working, this research proposes artistic strategies that learn to occupy rather than display the accident