Introduction to Special Issue of AI & Society by Springer. The journal includes contributes by Paglen&Crawford, Pasquinellli&Joler, Mirzoeff, Parikka, Bratton, Parisi, Manovich, Maleve, and more. The printed volume will come out in June 2020. On springer webpage it's possible to already access the articles selected for the issue. https://link.springer.com/journal/146/volumes-and-issues/36-4 How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. As the title makes clear, we take our point of departure in John Berger’s 1972 BBC documentary series Ways of Seeing, a four-part television series of 30-min films created by Berger and producer Mike Dibb, which had an enormous impact on both popular and academic perspectives on visual culture. Berger’s scripts were adapted into a book of the same name, published by Penguin also in 1972. The book consists of seven numbered essays: four using words and images; and three essays using only images. Seeing is evidently a political act, exemplified in the third episode-chapter, where images of women in early modern European painting (Pol de Limbourg, Cranach the Elder, Jan Gossaert, Tintoretto) and commercial magazines are juxtaposed to demonstrate the ways in which women are rendered as objects of the male gaze. More broadly, Berger emphasised that “the relation between what we see and what we know is never settled”. In this special issue, we explore how these ideas can be understood in the light of technical developments in machine vision and algorithmic learning, and how the relations between what we see and know are further unsettled.

This presentation analyses three recent works of art that interrogate the relationship between human perception and machine vision: Nadav Assor's art-documentary Lessons on Leaving Your Body (2014), Muse's VR music video Revolt (2015), and Erica Scourti's Body Scan (2014). How do these works present the relationship between human and machine vision? When machines can see us, do we see them as subjects in their own right, or as expansions of our human selves? The paper shows how the three works discussed portray machine vision in three different ways: as an expansion of human perception, as a hostile, controlling force that should be destroyed, and as a commercialised force altering or co-constructing the way we view our own humanity. 'Now objects perceive me,' the painter Paul Klee once wrote in his notebook, according to Paul Virilio in The Vision Machine (1994, orig. 1988). With the Internet of Things, objects that perceive us have become reality. Cameras watch us from satellites and drones, sharing information and using facial recognition algorithms to track individuals. Home surveillance systems measure air quality and send messages to parents when their facial recognition algorithms identify that a child has returned home from school. Alexa and Siri listen for our voices and answer our questions with information from the cloud. Up until the last few decades, human vision was the only visual perspective available to us. Visual technologies such as drawing, photography and video give us an indirect access to other humans' visual experience of the world, but still controlled by humans. We know other animals have different senses than us, but do not have direct access to their visual experience. We have only had direct access to our own visual experience, and indirectly, through the technologies of cameras and drawings, to that of other humans' visual experience. Now we also have access to a nonhuman form of vision: the machine vision of drones and satellites and surveillance cameras. Machine vision is the registration, analysis and representation of visual information by machines. Pre-digital technologies such as cameras gave us a taste of how machines perceive the world, but today's algorithms go even further in pushing us to see machines as perceiving beings with some kind of agency. Of course visual machines and the software they run are built by humans, but we have designed our machines so that they are increasingly autonomous (Flusser 2000). We have also designed our laws and regulations to increase the autonomy of machines. For example, international maritime regulations require ships to use electronic chart display and information systems (ECDIS), which leave human captains and navigators acting as supervisors watching screens rather than being active navigators. Surveillance systems in cities or homes automatically track individuals over time and space, only alerting human operators if something unusal occurs (Andrejevic & Burdon, 2015). Self-driving cars similarly rely on visual sensors and other positioning systems to navigate through traffic, and humans need only take action if something goes wrong. Google Photos sorts through our personal photographs and generates videos and animations from them based on image recognition algorithms, and then asks us which of its creations we wish to keep. In these ways machine vision becomes primary, with humans only being notified when the machine identifies something it has perceived as being valuable or dangerous.

It has been suggested by a range of established commenters that digital technology may have potentially created a ‘mental change’ within the creative process of making images and objects. Although the statement is somewhat broad and our ability to understand change often requires a certain amount of time to have passed (before the significance of an event maybe better understood) the compulsion to begin considering these ruminations became central to the ‘Looking Through The Eyes Of Machines As Students’ project. The root of this inquiry has predominantly developed through my teaching experience in the graphic arts and what it means to think through an established discipline in a technological age of multifaceted practice and outcomes. The working publication includes Q & A with a range of Graphic Arts students followed by my own thoughts on what a mental shift may be,

Today, computer vision is broadly implemented and operates in the background of many systems. For users of these technologies, there is often no visual feedback, making it hard to understand the mechanisms that drive it. When computer vision is used to generate visual representations like Google Earth, it remains difficult to perceive the particular process and principles that went into its creation. This text examines computer vision as a medium and a system of representation by analyzing the work of design studio Onformative, designer Bernhard Hopfengartner and artist Clement Valla. By using technical failures and employing computer vision in unforeseen ways, these artists and designers expose the differences between computer vision and human perception. Since computer vision is increasingly used to facilitate (visual) communication, artistic reflections like these help us understand the nature of computer vision and how it shapes our perception of the world.

Machine vision technologies are increasingly ubiquitous in society and have become part of everyday life. However, the rapid adoption has led to ethical concerns relating to privacy, bias and accuracy. This paper presents the methodology and some preliminary results from a digital humanities project that is mapping and categorising references to and uses of machine vision in digital art, narratives and games in order to find patterns that may help us understand the broader cultural understandings of machine vision in society. Understanding the cultural significance and valence of machine vision is crucial for developers of machine vision technologies, so that new technologies are designed to meet general needs and ethical concerns, and ultimately contribute to a better, more just society.

In many critical investigations of machine vision, the focus lies almost exclusively on dataset bias and on fixing datasets by introducing more and more diverse sets of images. We propose that machine vision systems are inherently biased not only because they rely on biased datasets but also because their perceptual topology, their specific way of representing the visual world, gives rise to a new class of bias that we call perceptual bias. Concretely, we define perceptual topology as the set of those inductive biases in machine vision systems that determine its capability to represent the visual world. Perceptual bias, then, describes the difference between the assumed "ways of seeing" of a machine vision system, our reasonable expectations regarding its way of representing the visual world, and its actual perceptual topology. We show how perceptual bias affects the interpretability of machine vision systems in particular, by means of a close reading of a visualization technique called "feature visualization". We conclude that dataset bias and perceptual bias both need to be considered in the critical analysis of machine vision systems and propose to understand critical machine vision as an important transdisciplinary challenge, situated at the interface of computer science and visual studies/Bildwissenschaft.