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The urgency of environmental, security, economic and political crises in the early twenty-first century has propelled the use of machine vision to aid human decision-making. These developments have led to strategies in which functions of human intuitive processing have been externalized to ‘vision machines’ in the hope of optimized and objective insights. I argue that we should approach these replacements of human nonconscious functions as ‘intuition machines.’ I apply this approach through a close reading of artworks which expose the hid- den labour required to train a machine. These artworks demonstrate how human agency shapes the ways that machines perceive the world and reveal how values and biases are hardcoded into nonconscious cognitive machine vision systems. Thus, my analysis suggests that decisions made by such systems cannot be considered fundamentally objective or true. Nevertheless, artworks also exemplify how externalized intuitive processing can still be helpful as long as we refrain from blindly taking the results as a go-signal to take immediate action.

Scambaiters are individuals in online information communities specializing in identifying, documenting and reporting actions of so-called '419 scammers'. A qualitative research approach was applied to the two active scambaiting communities -419eater.com and thescambaiter.com. Content analysis of several discussions and the examination of interviews from the web radio 'Area 419: Scambaiting Radio' resulted in the seven categories of scambaiting techniques that are presented in this paper. Our aim is to both give a wider understanding of the scope of existing Internet scams as well as answering questions of why and how individuals or communities of scambaiters take action against Internet scammers. The analysis on various scambaiting practices is intended as a base for future discussions, for instance, whether some scambaiting methods should be implemented in media competence training.

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.

This dataset captures cultural attitudes towards machine vision technologies as they are expressed in art, games and narratives. The dataset includes records of 500 creative works (including 77 digital games, 190 digital artworks and 233 movies, novels and other narratives) that use or represent machine vision technologies like facial recognition, deepfakes, and augmented reality. The dataset is divided into three main tables, relating to the works, to specific situations in each work involving machine vision technologies, and to the characters that interact with the technologies. Data about each work includes title, author, year and country of publication; types of machine vision technologies featured; topics the work addresses, and sentiments associated with that machine vision usage in the work. In the various works we identified 874 specific situations where machine vision is central. The dataset includes detailed data about each of these situations that describes the actions of human and non-human agents, including machine vision technologies. The dataset is the product of a digital humanities project and can be also viewed as a database at http://machine-vision.no. Data was collected by a team of topic experts who followed an analytical model developed to explore relationships between humans and technologies, inspired by posthumanist and feminist new materialist theories. The dataset as well as the more detailed database can be viewed, searched, extracted, or otherwise used or reused and is considered particularly useful for humanities and social science scholars interested in the relationship between technology and culture, and by designers, artists, and scientists developing machine vision technologies.