Filed under report | Tags: · artificial intelligence, automation, data, employment, ethics, gender, governance, infrastructure, machine learning, policy, power, race, society
This report “examines new research on the risks and harms of AI, including its use by companies to aggressively manage and control workers, its climate impact, and the growing use of facial and affect recognition. We also look at the growing movements that are demanding a halt to risky and dangerous AI, and offer recommendations on what policymakers, advocates, and researchers can do to address these harms.”
By Kate Crawford, Roel Dobbe, Theodora Dryer, Genevieve Fried, Ben Green, Elizabeth Kaziunas, Amba Kak, Varoon Mathur, Erin McElroy, Andrea Nill Sánchez, Deborah Raji, Joy Lisi Rankin, Rashida Richardson, Jason Schultz, Sarah Myers West, and Meredith Whittaker
Publisher AI Now Institute, New York, 12 Dec 2019
Creative Commons BY-ND 4.0 International License
Filed under thesis | Tags: · algorithm, art, automation, control society, generativity, governmentality, labour, lecture-performance, machine learning, neural networks
“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.”
Publisher Faculty of the Victorian College of the Arts and Melbourne Conservatorium of Music, The University of Melbourne, 2019
Filed under journal | Tags: · aesthetics, affect, feeling, machine, machine learning, sensation, subjectivation, technology
“Digital culture has become instrumental for capturing and managing what Raymond Williams would once have called “structures of feeling”. The journal issue A Peer-Reviewed Journal About Machine Feeling alludes to this, and points to a material analysis of aesthetics and culture, including its technical and social forms, and in the way that this concept was originally employed as an acknowledgment of the importance of the hard to capture dimensions of everyday life. What potential new sensibilities and structures of feeling may arise in such normalized registers of our habits? What new cultural and social forms and practices emerge in the coming together of machine learning and structures of feeling? In each their own way, the authors in this journal explore these questions.”
Edited by Christian Ulrik Andersen and Geoff Cox
Publisher Digital Aesthetics Research Centre, Aarhus University, Aarhus, 15 August 2019
Creative Commons BY-NC-SA License
With contributions by Mitra Azar, Daniel Chávez Heras, Michela De Carlo, Iain Emsley, Malthe Stavning Erslev, Tomas Hollanek, Rosemary Lee, Carleigh Morgan, Carman Ng, Irina Raskin, Tiara Roxanne, Rebecca Uliasz, Maria Dada, Tanja Wiehn, and Brett Zehner.Comment (0)