Human-Aided Artificial Intelligence: Or, How to Run Large Computations in Human Brains? Towards a Media Sociology of Machine Learning
Forthcoming in New Media & Society, OnlineFirst Nov. 2019. doi: 10.1177/1461444819885334
20 Pages Posted: 3 Apr 2020
Date Written: 2019
Abstract
Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users.
Keywords: Artificial Intelligence, Deep Learning, Social Media, Human Computation, Commercial Content Moderation, Human-Computer Interaction, User Experience Design, Big Data, Tracking, Training Data, Cybernetics
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