Machine vision situations: Tracing distributed agency.

This article proposes a new method for tracing and examining agency in heterogeneous assemblages, focusing on the role of machine vision technologies in creative works. We introduce the concept of the "machine vision situation" and define it as the moment in which machine vision technologies come into play and make a difference to the course of events. By taking situations as the unit of analysis, we identify moments at which machine vision technologies take part in actions without reducing them to either tools or protagonists, instead allowing for more complex agential entanglements between human and non-human actors. Grounded on an interdisciplinary theoretical framework, this article demonstrates how an analytical unit such as the machine vision situation is a valuable method for tracing how agency is distributed. We illustrate this through three examples by applying the method to creative works - narratives, digital games, and artworks - revealing key aspects of distributed agency and calling attention to the excess, complications, and messy entanglements that might otherwise be overlooked in analyses of agential assemblages. The machine vision situation is shown to be a flexible unit of analysis that can be productively incorporated in both quantitative and qualitative studies and applied to other contexts in which human and non-human agencies interact.

Machine vision ­ the ability of machines to "see" and interpret visual information ­ has advanced significantly in recent years, with applications ranging from self-driving cars to medical diagnosis. However, there is a growing recognition that this technological advancement does not simply power a wide variety of new tools and systems, but also results in new distributions of agency alongside (and, at times, against) human decision-making. Our article explores this idea in depth, examining how machine vision technologies and human beings are represented as agents in works of narrative such as games, art, and fiction. Analysing the representation of machine vision in artistic works reveals how these technologies are experienced and imagined in different contexts. We introduce the framework of "machine vision situations" to analyse the complex and dynamic relationships between humans and machines in both fictional and real-world contexts. A machine vision situation is a moment in which a machine vision technology is seen or represented as making a difference to the course of events. This situation can be analysed by identifying the actors involved and making a list of verbs that describe each of their contributions to the event. This method results in a dataset that can be analysed quantitatively, but it also generates a starting point for a qualitative analysis of distributed agency between human and non-human agents in both fictional and real-world situations.

Representations of machine vision technologies in artworks, games and narratives: A dataset.

This data paper documents a dataset that 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 include title, author, year and country of publication; types of machine vision technologies featured; topics the work addresses, and sentiments shown towards machine vision 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 is 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.

Ask Rosa - The making of a digital genetic conversation tool, a chatbot, about hereditary breast and ovarian cancer.

OBJECTIVE: We aimed at developing a pilot version of an app (Rosa) that can perform digital conversations with breast or ovarian cancer patients about genetic BRCA testing, using chatbot technology, to identify best practices for future patient-focused chatbots. METHODS: We chose a commercial chatbot platform and participatory methodology with a team of patient representatives, IT engineers, genetic counselors and clinical geneticists, within a nationwide collaboration. An iterative approach ensured extensive user and formal usability testing during the development process. RESULTS: The development phase lasted for two years until the pilot version was completed in December 2019. The iteration steps disclosed major challenges in the artificial intelligence (AI)-based matching of user provided questions with predefined information in the database, leading initially to high level of fallback answers. We therefore developed strategies to reduce potential language ambiguities (e.g. BRCA1 vs BRCA2) and overcome dialogue confusion. The first prototype contained a database with 500 predefined questions and 67 corresponding predefined answers, while the final version included 2257 predefined questions and 144 predefined answers. Despite the limited AI functionality of the chatbot, the testing revealed that the users liked the layout and found the chatbot trustworthy and reader friendly. CONCLUSIONS: Building a health chatbot is challenging, expensive and time consuming with today's technology. The users had a positive attitude to the chatbot, and would use it in a real life setting, if given to them by health care personnel. PRACTICE IMPLICATIONS: We here present a framework for future health chatbot initiatives. The participatory methodology in combination with an iterative approach ensured that the patient perspective was incorporated at every level of the development process. We strongly recommend this approach in patient-centered health innovations.