Internet of things:
Dystopian Artificial Intelligence, Black Boxes.
Track chairs Alia Ghaddar (International University of Beirut), Fadi Yammout (International University of Beirut), Helena
The Internet of Things (IoT) is increasingly integrated with the daily life real world. It is playing an essential role in the advancement of living spaces from smart buildings to smart cities. Beyond the current hype, IoT is undoubtedly affecting all sectors at a rapid pace: companies, industries, and the economy. This track looks to address the critical role that IoT plays in the next generation information and communication systems. The aim is to highlight the opportunities that IoT creates for new products, services, and business models and how people harness its potential (creating smarter products, delivering intelligent insights and providing new business outcomes).
AI technology merges with the IoT from smart thermostats to wearable healthcare devices to smart-camera and server for surveillance systems integrating AI face-recognition capable of detecting gender, age, and emotions. Every image uploaded to the internet is first seen by a machine’s eyes before human ones, we can say the same about many devices within the Internet of Things: many streams from IP-cameras are processed by machine vision: registered, analyzed, recognized, or even judged. And very soon, all content and information from the physical world captured by the ubiquitous IoT devices will be perceived by AI cloud processing. IoT is not a merely a step towards smart cozy houses and cities, but rather a means to gather data of our presence and actions in the physical world.
IoT and AI together become a potential new tool for algorithmic regulation. This track explores the IoT not only as Michel Foucault’s Panopticon but rather as a tool of biopolitics, a system of surveillance and control and a way of nudging citizens towards preferable behavior, instead of trying to understand and deal with the root causes of social problems.
The current impetus to ‘open the black box’ of contemporary machine learning has so far left unaddressed the timelier challenge of operationalizing the opacity of these systems. By investigating the modes of governmentality and cultural practices embedded in machine learning devices, this track will aim at reconstituting some of the conditions through which these systems perceive, classify, and operate upon the objects and subjects they engage with. As a way to trouble the cultural imaginaries around machine learning, this track will investigate the larger political aesthetics of the data practices at the heart of today’s digital infrastructures and cultures.
While first conceived by cyberneticists as a functional model of heterogeneous systems (Ashby 1956, Wiener 1948), the black box has become a key concept to account for opaque media objects including machine learning (Mackenzie 2017) and algorithms of all kinds (Bratton 2016). Behind the impetus to ‘open the black box’ (Latour 1987) however lurks the assumption that there is indeed something to be seen within these opaque systems, reinstating visual knowledge as the privileged way to know objects that otherwise resist visuality and representation (Chun 2011). The persistence of the black box as a theoretical concept thus calls for a larger investigation of the visual bias underpinning how opacity is conceived and operationalized in fields like algorithmic art, generative modeling, and media studies.