Introduction
Machine vision, understood as “the registration, analysis, and representation of visual data by machines and algorithms” (Rettberg et al., 2019, 1), brings us sights (the inside of one’s colon, the travel patterns of a population under lockdown, or a live feed of owl hatchlings huddled in a bird box) which we would never have been able to observe without technological mediation. The history of machine vision can be traced back to the invention and popularisation of the camera and can arguably be extended to include the history of glasses, binoculars, microscopes, and other noteworthy early examples of technologies that have enabled us to surpass the limitations of embodied human vision. Developments in algorithmic automation and machine learning dramatically broaden the extent to which machine vision reaches into our lives. Technologies that fall under the umbrella of machine vision encompass widely different objects and uses ranging from surveillance cameras and webcams to facial recognition and cancer detection. The computational processes underpinning much of machine vision also make possible the creation of synthetic images such as deepfakes, multimodal machine learning models that generate images from written prompts, and augmented reality applications such as amusing smartphone camera filters.
The proliferation of machine vision agents and processes raises new questions about agency. Who creates the image when you type a prompt into an image generator like DALL-E or Midjourney? Is it you, or the machine learning model? Who is responsible when a person is wrongfully arrested after being misidentified by a facial recognition algorithm? Is it the police, the algorithm, or its programmers? The police, the algorithm, or its programmers? Advancements in machine learning are driving the development of increasingly autonomous technological agents: self-driving cars are built with the ability to act upon information in real time with no or minimal human input, and generative models reconfigure the relationship between human and non-human creativity and labor. How can we understand the distributions of agency between human and technological actors such as machine vision systems? How can we identify the contributions of each agent without losing track of their connectedness? How are human and non-human actors affected or changed by their interactions with machine vision? In other words, how do we understand agency and responsibility when automated entities interpret, act upon and even create visual data?
Our answer, which we develop throughout this article, is based on the premise that agency is not an exclusively human capacity. The notion of non-human agency has been developed across a wide range of theories and approaches that cluster around the fields of posthuman theory, new materialism, affect theory, and science and technology studies (Bennett, 2010; Hayles, 2017; Latour, 2005; Massumi, 2021). To understand agency in this broader sense, we need to take account not only of human choices and actions, but also of those of other lifeforms such as plants, animals, and microorganisms, as well as of non-living matter such as technical objects, weather phenomena, and other “things” (Hayles, 2017). Acknowledging the agency of the non-human world undermines the position of the (hu)man as the sovereign actor who controls and transforms nature through the deliberate use of tools (Marchand, 2018). What used to be a passive backdrop for human heroism comes to life, and the stage becomes populated by a proliferation of agents, allowing new dramas to play out. By focusing on a specific domain of technology and representation – the role of machine vision in cultural production - this article proposes a method for bringing these dramas to light.
When studying the distribution of agency among human and non-human actors, it is essential to develop research methods that do not rely on a binary opposition between human and non-human actors nor operate with the assumption that agency is a zero-sum game in which the agency of one actor automatically diminishes or cancels out the agency of others. Drawing on Lauren Berlant’s (2011) use of the situation as an analytical tool, we propose the concept of machine vision situations, defined as the moment at which machine vision technologies come into play and are seen to make a difference in what is taking place. Based on the concept of machine vision situations, this paper develops a method for tracing the distribution of agency between human and non-human actors, discusses its relevance to different approaches to non-human agency, and demonstrates its potential for both quantitative and qualitative analysis of distributed agency in other contexts.
Technological change is always accompanied by public debate. We argue that fiction, art, games and cultural expression more broadly are modes of discourse that allow societies to access responses to technology that are not always expressed in rational debates (Rettberg, 2023:13–15). Many existing technologies were first described in science fiction or myths: technological development is often driven by fiction and imagination (de Seta, 2022). Playing games or watching a movie allows people to become familiar with technologies before they have direct access to them (Solberg, 2022a). Technologies are critiqued and challenged in art games and narratives, since art, in the broadest sense of the word, can express emotional responses to technological change as well as exploring what could go wrong or how technologies could be used differently (Gunderson, 2021; Kronman, 2023).
The rapid development of AI-powered machine vision technologies like facial and object recognition, drones, image generation, deepfakes and surveillance has led to governments and organisations around the world implementing guidelines, policies and regulations addressing the challenges they bring. The machine vision situations method brings out insights from fiction, art, games and other aesthetic modes of expression that are often underexamined in academic and political deliberations, and can thus add to the knowledge used by policy makers and developers to guide the development and application of artificial intelligence and machine vision technologies.
In the following sections, we begin by reviewing theoretical approaches including Bruno Latour’s actor-network theory (Latour, 2005), N.K. Hayles’ concept of nonconscious cognition (Hayles, 2017), and Lauren Berlant’s (2011) and Brian Massumi’s approaches to affect (Massumi, 2021). We then trace how we conceptualized the machine vision situation as an analytical unit, outline the method we developed around it, and discuss how it informs and is informed by multiple approaches to non-human agency. This discussion is followed by three examples of machine vision situations from Annalee Newitz’s short story Drones Don’t Kill People (2014), the digital game Detroit: Become Human (2018), and Anna Ridler’s digital artworks Myriad (Tulips) (2018) and Mosaic Virus (2019a), illustrating how this tool can be used to analyse narrative works, games, and artworks. Finally, we discuss how this tool may be used beyond the context of creative works and point towards their further development.
Machine vision situations
We developed the concept of the machine vision situation as we worked on the Database of Machine Vision in Art, Games, and Narratives. Completed in 2021, the database documents 77 digital games, 190 digital artworks, and 233 movies, novels, and other narratives that use or represent machine vision technologies (Rettberg et al., 2022a; Rettberg et al., 2022b). Within a total corpus of 500 works, we have identified and analysed 874 specific situations where machine vision is central (Rettberg et al., 2022a; Rettberg et al., 2022b). Based on the definition of machine vision mentioned above, and an initial survey of creative works, we developed a list of 26 different technologies that formed the basis for identifying machine vision situations in the works.1 Our goal in developing the database was to draw on the computational powers of the digital humanities to conceptually map commonalities and tendencies in representations of machine vision across a wide range of works. This is a common approach in the digital humanities (see, for instance, Sinclair & Rockwell, 2015, 288). Computational methods of analysis have often been placed in opposition to “the individuated and situated practices of human reading and interpretation” (Drucker, 2017, 631), but as Drucker points out, this is a false dichotomy. As Jill Walker Rettberg argues, “data is always partial and situated” (Rettberg, 2020, 4), and designing a database or a text analysis program is an interpretative act (Drucker, 2017), something the lively discussions within our research group can attest to. Designing a system in which to characterise often complex works through a limited number of keywords is a process of making careful trade-offs between specificity and generalisability. While many digital humanities projects rely on machine reading and interpretation of a corpus, our project relied on “manually” selecting the works to include in the corpus. The database structure functioned as a lens through which we constructed a body of data that would then be analysed using digital tools. However, the actual data resulted from each researcher’s reading and interpretation of the works. The process thus combines classical humanistic, interpretative approaches to text with digital, quantitative methods.
In order for works to be registered in a database, there must first be a conceptual infrastructure (Feinberg, 2017) within which to register them. This process involved deciding on the categories under which works would be classified. To see patterns across genres and technologies, we needed a classification schema that allowed us to find shared characteristics in a very diverse dataset. The challenge was finding a format that allows for direct and easy comparison between the works required the design of a structure through which we could extract comparable meanings from, for example, a digital art installation, a digital game, a movie, a novel, and a piece of digital fiction. In our case, this meant negotiating and discussing which aspects of the works had relevance for the project and deciding on the basic units of meaning through which to build a corpus of data that would lend itself to network analysis by making some features of the text itself explicit, with the goal of making them processable by some computer application (Renear, 2004 in Pierazzo, 2015:307). This infrastructure, for us, is built around the situation.
A situation, according to Lauren Berlant, is “a state of things in which something that will perhaps matter is unfolding amid the usual activity of life” (Berlant, 2011:5) and “a genre of living that one knows one’s in but that one has to find out about, a circumstance embedded in life but not in one’s control” (2011:195). This concept of the situation as a moment of disturbance in everyday life that “forces one to [...] become interested in potential changes to ordinariness” (2011:195) is a very fruitful concept with which to analyse the cultural imagination of emerging technologies, such as machine vision. The task is to identify the situations in which machine vision technologies come to matter in the action of what is unfolding within or through the work. A machine vision situation may be an excerpt from a novel, a scene in a movie or a sub-plot in a game, or it may encompass a whole work, as may be the case with short stories or artworks – what we were looking for was the moments at which machine vision technologies were taking part in the action, so to speak. Each work may contain one or more of these situations in which one or more machine vision technologies come into play.
The focus on the specific situation is also inspired by Donna Haraway's concept of situated knowledges (Haraway, 2007), and our research is grounded in the premise that knowledge about sociotechnical processes will always be rooted in specific contexts. Instead of aiming for the objectivity of a God-like view from nowhere (Haraway, 2007, 115), our aim was to develop a method committed to the particular, contextual, and diverse practices and meanings that emerge in specific circumstances. This commitment may seem contradictory to a database-based partly quantitative approach, in which information is extracted from its original context and made to fit into a pre-determined structure. However, this tension turned out to be a generative force that nudged us to circle back to the data and developing close readings of individual machine vision situations.
By defining machine vision situations as a core unit of the database, we identified moments where machine vision technologies became what Bruno Latour would call “matters of concern” (Latour, 2004). In Reassembling the Social (2005), Latour argues that accounts of social agency are limited by their reliance on a conception of the social which precludes the inclusion of anything, or any thing, that is not composed, collectively or individually, of people. This social realm is seen as separate and clearly distinguishable from other realms of reality, such as physics, biology, or geology. In actor-network theory, which Latour developed in the 1980s and 1990s with Madeleine Akrich, John Law, Michel Callon and others (Akrich, 2023), the social is redefined as “a type of connection between things that are not themselves social” (Latour, 2005:5) and the task of the social scientist is redefined as “the tracing of associations between heterogeneous elements” (Latour, 2005:5). Action, in the actor-network approach, is no longer limited to that which is intentional or meaningful human behaviour, but may just as well rest in “the domain of ‘material’ ‘causal’ relations” (Latour, 2005:71). If something, or some thing “makes a difference in the course of some other agent’s action” (Latour, 2005:71), that thing has agency within the context of which it is seen to matter. Building on this, the idea of machine vision situations is that looking at the doings of humans and technologies together allows us to see the difference each agent makes in the situation and to understand the specificity of their interactions. While actor-network theory has developed its own methodological tools for studying the interrelations of human and non-human agency, for our purpose we needed to develop a system that would allow us to generate data that could be studied quantitatively and which was suited to analyse works of fiction, games, and art. In order to capture these interactions within the various works, we set out to establish a model with which to encode the actions and actors involved that was simple enough to use as the structure of our database.
Another major challenge was figuring out how to capture meaningful interactions across a wide range of machine vision technologies. Describing the agency expressed by a specific machine vision technology within a work was complicated by our realisation that machine vision technologies are often presented as doing many different things that cannot be easily reduced to a single entry. It was clear that we could not form our analysis of machine vision simply around the act of seeing. Some actions might more easily be described as generating, classifying, or identifying, but even that seemed insufficient to capture what these devices were doing in our lives on a meaningful level. The same technology may do different things in different contexts or different works. However, it is easy to take the agency of non-organic objects as given by their known functions and uses. As Massumi states, “a thing is when it is not doing” (2021:7), meaning that when an object is not doing, it is fixed, known, static. However, we are interested in the doing, the undetermined potentiality for action, and their ability to cause something to happen, without assuming to know in advance the difference they would make. Instead of looking at machine vision technologies in their “thingliness”, as objects with known uses and fixed functions, we set out to study their manifold actions as embodied and/or articulated in a wide range of works. As a result, we decided to describe what technologies and other actors are doing in each situation through an open vocabulary of verbs. In the case of the machine vision database, this ‘verbosity’ may be an apt method to trace the difference these technologies make as linguistic markers of our vibrant relationship with that which we are used to thinking about as matter, resources, or tools. By attributing verbs to machine vision technologies, they become visible as agents within these situations.
Following Latour (2005), in order to understand the unfolding of social (and non-social) events, the contributions of each element in the chain, or network, of action must be taken into account: the researcher should trace the connections that enable social agency, through a network composed, as it may be, of objects, groups, documents, and people. We landed on a system in which we registered one or several machine vision technologies and any characters and/or entities that were active in each machine vision situation.2 We used the term character as it is used in narratology: “a text- or media-based figure in a storyworld, usually human or human-like (..) in contrast to ‘persons’ as individuals in the real world” (Jannidis, 2014). In our dataset characters were often human, but many were also robots, sentient AIs, or as in one of the example analyses later in this paper, sentient drones. We registered information about how characters in each situation were represented, such as their gender3, age, etc. Entities are objects, institutions, or generic categories such as “users” or “images” for which gender or similar characteristics are not relevant. Each agent (a technology, character, or entity) was then assigned a verb to describe their interaction with the other agents in the situation. This enabled us to capture some of their liveliness, while remaining open to the possibility that technologies, people, and other entities may be seen to be doing several things at once, without predetermining the types of actions that may be taken by any of the actors involved.
We made a deliberate choice to position technology, people, and institutions on the same level in the machine vision situation. Our analytical model has no predetermined structure that pre-defines technologies as passive, or as tools or objects, or that positions humans as users, creators, or subjects. This even-handed treatment allows both machines and people to be assigned agency in a given situation. Suddenly, their active participation in a chain of events comes to the foreground. The analyses of machine vision situations later in this paper demonstrate how this works in practice.
Studying humans, technologies, and other entities as potentially lively “bodies with the power to affect and be affected” (Massumi, 2021:16) in unforeclosed ways also required a system for capturing the effects other actors have on each entity involved in a situation. We used the present continuous tense (e.g. watching) for actions undertaken by a character, technology, or entity, and the past participle as used in the passive tense (e.g. watched) for actions that happen to a character, technology, or entity. This enabled us to identify the various activities that machine vision technologies are involved in, as well as their effects on the various actors involved. This inclusion of passive verbs reveals how the various agents in the situation are affected by each other’s actions, bringing out how they are both being changed and effecting change on other bodies within the situation.
In the database, however, the technologies (alongside the characters and entities) appear as separate, predefined categories. This is a departure from the actor-network-theory approach of allowing the subjects to appear as you trace their interactions. By designating them with controlled vocabularies, the actors appear more fixed and stable than they might have any reason to be. The limitations of the understanding of agency imposed by the database structure are particularly apparent when seen through the lens offered by Jane Bennett in her book Vibrant Matter. Building on Latour, she theorises non-human agency through the Deleuzian concept of assemblages, “ad hoc groupings of diverse elements” (Bennett, 2010:23) through which agency is effectuated and unfolds. Agency, for Bennett, is not controlled from one central node; rather, the ability to make something happen may arise from the emergent properties of assemblages of matter, bodies, and forces. The machine vision situation is our attempt to map some of these assemblages and trace the agency as it emerges between and among the diverse bodies involved. The verbs attached to the various agents highlight the distribution of agency throughout the assemblage, bringing out their diverse contributions of causes and effects while demonstrating their interconnectedness.
Although our conceptualization of situation emerged from the process of designing a research database, it shares several aspects with the method of situational analysis developed by Adele Clarke and several other scholars. Clarke positions situational analysis as “an extension of grounded theory (GT) method of analysis for qualitative research” (2016:1) in which “the situation itself becomes the unit of analysis, and the researcher maps the situation to analyze it” (1), and which can be applied to multiple kinds of data, including interview transcripts, ethnographic notes, visual content, and so on. Situational analysis is underpinned by a wide range of theoretical approaches across disciplines, including interactionist sociology, feminist embodiment, and infrastructural ecology (Clarke et al., 2022:25), as its unit of analysis – the situation – is conceived in ecological terms as being a composition of co-constitutive elements (Clarke, 2019:15). While sharing with situational analysis this centrality of the situation as an analytical unit, as well many theoretical interlocutors (including Massumi, Haraway, and Latour), the approach we propose in this article is designed to operate on a smaller scale. Rather than conceptualizing entire social arenas or research projects as a situation to be mapped, we identify situations in the scenes, events, or moments of interaction that are depicted in narrative works such as artworks, digital games or novels. This difference in scale reflects a broader methodological choice: rather than departing from grounded theory, our project applied a targeted approach to cultural representation through which we sought to identify the role of a specific technology (machine vision) in a heterogeneous corpus of texts. Despite the different scale at which our approach to situations operates, the method we propose can easily complement situational analysis by allowing researchers to identify pivotal moments or events and zoom into the details of what is happening in these more limited situations within a larger context.
To sum up – in order to study what is being done with, by, and to machine vision technologies, we begin by identifying situations in which these technologies are seen to make a difference. We then identify the agents involved in the situation – the specific technologies, characters, and other entities participating in the action. The next step is to look closely at the situation, and attribute verbs to each of the agents involved, based on what they are seen to be doing, using passive verbs to describe the effects of actions, if appropriate. The verbs should be allowed to emerge from the text(ure) of the situation, and the same agent may have several different, even contradictory, verbs attached to it. Relatedly, the verbs are not limited to the supposed or assumed uses or activities in which a body or object of its kind is assumed to take part.
In what follows, we will go through machine vision situations from three different works: a short story, a digital game, and an artwork, to demonstrate how this analytical model can be applied to explore the representation of agency in a variety of genres.
Example analyses: Assemblages in the works
Drones Don’t Kill People
The result of this analytical model is a snapshot of actions attached to each actor, which can, on their own, form remarkably compelling narratives. For example, in the short story Drones Don’t Kill People by Annalee Newitz (2014), narrated from the perspective of an artificially intelligent surveillance drone, one machine vision situation occurs when a group of largely autonomous AI drones, subcontracted to a corporation working for the Turkish government, are set to monitor a professor and his family and to pass on potentially relevant information. One night the drones observe the unnamed mother answering a question posed by the daughter. This is what the incident looks like in the database:
Professor’s Daughter: is Questioning
Professor’s Wife is: Answering, Explaining, Revealing
Drones are: Spying, Recording, Interpreting, Selecting, Debating
Corporation is: Subcontracting, Employing, Surveilling, Deciding
Here we see that although the drones perform expected machine vision actions such as recording, they are also partaking in other more cognitive and collaborative activities involved in the same process. The inclusion of “spying” furthermore emphasises that what they are doing has normative implications in a larger social context. The mother and daughter’s actions are linked to the drones’ activity through the inclusion of “revealing”. Finally, the corporation’s actions are included in the assemblage, highlighting that the drones are not acting in a vacuum. The corporation is the agent attributed with the action of “surveilling” since they, not the drones, decide what to do with the information which is collected. Together, they enter into what Bennett (2010) might describe as an assemblage of machine vision-mediated activity. However, this does not mean that agency is equally distributed among the actors, which will become evident in the next entry from the same short story.
Based on the information collected from this incident, the professor and his family are identified as political dissenters and activists, and the drones are ordered to assassinate them. In the database, the entry for this second situation looks like this:
Corporation is: Subcontracting, Employing, Deciding, Ordering
Drones are: Obeying, Deciding, Killing, Watching, Recording.
Professor is: Killed
Professor’s Wife is: Killed
Professor’s Son is: Killed
Professor’s Daughter is: Evading, Watching, Screaming, Killed, Recorded.
Both the drones and the corporation take part in the act of killing the professor and his family. The unequal distribution of power between the drones and the corporation is made explicit through the inclusion of the verbs “ordering” and “obeying,” while the inclusion of “deciding” emphasises the drones’ active role. Here, the act of killing is attributed to the drones, although this will be contested later in the story, as foreshadowed by the title. The assassination went according to plan, although it did include one “statistically anomalous event” when the drones missed the daughter on the first shot. Her screams of terror as she watched them kill her family were recorded in their distributed memory. The way this situation is written, the family, with the exception of the daughter, have very little agency, as their lives are extinguished before they can register what is happening or respond – as Bennett would say, while the drones are acting, they are “suffering action” (2010:21). The daughter suffers the same fate, but her final actions reverberate throughout the story – through the recording of the incident, her screams will continue to have the power to affect the drones.
The act of assigning the actors and verbs involved in a situation is inevitably interpretative, and revisiting these entries inevitably brings up potential edits and additions that could have been made. For instance, rereading the short story revealed a third situation, overlooked at the time of data entry, but readily apparent in the light of this paper: after the contract with the Turkish company expires, the drones are subcontracted to the Uyghur Republic4 government to monitor a desert highway in contested territory bordering on China. Little of relevance is happening, and for the first time, the drones are left alone with nothing to do, so they look about on the web for stuff to analyse. As drones, they are programmed to recognise faces, so when they come across images of drones with faces painted on them, they begin investigating their own identity.
Government is: Ordering, Surveilling, Ignoring
Drones are: Bored, Analysing, Sharing, Recognising, Changing
Images are: Fascinating, Recognised
In this situation, images appear as an agent for their capacity to capture the drones’ attention and elicit a moment of recognition and interest in themselves as drones. Here, something unexpected occurs: the drones take the first step towards self-awareness and, thus, consciousness. As a result, they become interested in themselves as agents in the world. This is the true turning point in the story, taking the drones down the path of forming a political movement fighting for the rights of AIs. The images are clearly making a difference to the drones’ course of action in the Latourian (Latour, 2005) sense, and in this assemblage, they are the pivotal actors.
In all three of these situations, something new emerges in the machine vision assemblage. In the first one, the ambiguity of the information makes the drones debate whether or not to include it in their report. In the second situation, the recording of the screaming daughter will reverberate in the drones’ collective memory. In the third situation, the moment of self-recognition prompts the emergence of self-awareness, opening the drones up to change and their emergence as political subjects. The assigned verbs do not necessarily capture these becomings as they are matters of affect, not action – they are what takes form beyond the actions taken by each of the entities of the situation, changes in positionings, and the seeds of future events (Massumi, 2021). Nevertheless, tracking these changes can point to moments where action and affect come together to make something new. This would not become apparent in a quantitative analysis of a great number of machine vision situations like the ones collected for the database. However, each situation is an invitation to look closer, follow the connections and interactions that become apparent through this method, and look for what emerges beyond the verbs.
Registering incidents in a short story using this framework may be relatively straightforward. However open to interpretation and shifting meanings, a text is still a relatively static object in the sense that the words appear in the same order every time you encounter them, and the narrative structure easily lends itself to the process of identifying actors and actions. In what follows, we will show how machine vision situations work when considering other genres: first, a game, and then two works of art.
Detroit: Become Human
The doing can take on various forms across media. Audiovisual media like films and digital games often present actions visually, and in most games, this visualisation will also require some sort of haptic action from its user. Using situations to trace agency in games shows these various entanglements of action and bodies across levels of virtuality.
Consider the game Detroit: Become Human, published in 2018 by Quantic Dream, which is a branching narrative game that follows androids as they navigate their emerging sentience in a society that places them in positions of servitude and submission. One of these androids is the police inspector Connor, whose job is to find and capture rogue sentient androids. Due to Connor’s android body, the presence of various machine vision technologies embedded in his body is unquestioned. Connor’s augmented vision includes an augmented reality overlay of the world, combined with object recognition technology, vision beyond the human spectrum, and even reconstructive/generative image software that can visualise past events leading up to the crime scenes before him. These technologies perform various actions, but are combined in the character of Connor, played by the player. Connor’s actions are also actions on behalf of the game software, the video game console, the player, and various machine vision technologies, showing the interdependence and permeable boundaries between various agents in the assemblage (Bennett, 2010).
In one situation, two guards escort Connor into a corporate elevator to present him to their boss. Once inside the elevator, a perceptive Connor/player can identify a surveillance camera in the top corner, hack into it, kill the two guards, and escape without repercussions. The situation entry looks like this:
Connor is: Hacking, Fighting, Cloaking, Killing
Surveillance camera is: Hacked, Blinded
Law enforcement is: Fighting, Killed
Corporation is: Blinded
Here, we see that the entry creates a directional relation between Connor’s hacking and the surveillance camera that is hacked. In turn, Connor’s hacking not only blinds the surveillance camera, but also the corporation to which it belongs. The surveillance camera becomes a Latourian (Latour, 2005) “thing” that influences another agent’s action. In assigning the same passive action of “blinded” to both surveillance camera and corporation, a temporal relation is created between the two. This relation emphasises how the surveillance cameras are the prosthetic eyes of the corporation in this situation.
Many games offer diverging and possibly mutually exclusive narrative paths, and Detroit: Become Human is no exception, as it is structured around presenting influential narrative choices to the player. If Connor does nothing in the elevator, he will be shot and killed when the doors open again. If Connor attacks the guards in the elevator without disabling the surveillance camera, he will have to fight more guards upon exiting, because he will have been spotted acting deviantly, i.e. more sentient than the humans prefer. Connor can also fail at fighting the guards. Following this pattern of diverging paths, the elevator situation will not even occur in some playthroughs because it depends on previous choices.
The science fiction world of Detroit: Become Human exemplifies the characterisation of technologies. In this situation, Connor is the agent, not the various processes that combine into him. The player’s bodily labour of choosing and possibly failing at performing actions is hidden in this particular situation analysis. This reveals how human and non-human agencies combine or work in tandem beyond the fictional world. Even if the player is not an explicit agent in this situation, they are implicit in the presence of the player character’s actions. In a different context, the situation could be expanded to include the player, as well as the console, or even the company or programmers who made the game, and so forth – one could follow these connections forever.
This method also highlights the challenge of determining which actors are registered and are thus made visible. The choices of what to include as an actor would inevitably affect the dynamics in a given situation. For instance, by including the player in the above situation, we would not only introduce a set of new actions, but also new tensions, meanings and potential readings of the situation. In the case of Detroit: Become Human, this inclusion could bring out issues of race and structural oppression, since in this game, androids are treated like an oppressed minority in ways that mirrors the struggles of racialized minorities in the U.S (Dehnert & Leach, 2021; Leach & Dehnert, 2021; Schubert, 2021), and the inclusion of the player as an actor could bring up interesting tensions and contradictions between the player’s identity and experiences and that of the player character. However, in this project, we decided to solely include the actors represented within the work, which means that the database does not register the player or the console here, but the combined action of distributed agents, as represented by Connor. For our purposes the player is included only when they directly interact with machine vision, for instance with the augmented reality display of Pokémon Go (Niantic, 2016). This decision arose out of the need to have situations be comparable across genres and types as work, and our focus on the representation of technologies within the contents of the works themselves. Although this means that we missed out on some potential insights related to the embodiment of the player, this was a necessary trade-off given the thematic scope of this project. A project with different research focus and priorities would most likely make different decisions about which actors to include or exclude, as the boundaries of the assemblage must be drawn with concern for the specific purpose of the knowledge project of which it is a part.
Myriad and Mosaic Virus
In art, situations allow multiple layers of analysis. Digital artworks can put the viewer in a situation where they interact with machine vision technologies. They can represent fictional or actual machine vision situations in the same way as games and narratives do. Digital artworks also often highlight their own creation, that is, the situation in which the artwork was created. Anna Ridler’s diptych Myriad (Tulips) (2018) and Mosaic Virus (2019a) comprises two of many artworks where artists use machine learning-based AI image generation to create art. Myriad (Tulips) exhibits a dataset of hand-labelled polaroids classifying – according to colour, type, and stripe – a myriad of tulips, that is, ten thousand tulips. AI image generation often appears to be an automated process, but as Ridler describes it, dataset curation involves an “insane amount of work and it is usually work that is hidden” (Ridler, 2019c). To create the dataset, Ridler selected the tulips at the market, photographed them, and sorted and labelled them. Thereafter, Generative Adversarial Networks (GANs), a type of machine learning model, were trained with the dataset. What the network learned from the ten thousand images of tulips was to hallucinate “botanical impossibilities” (Ridler, 2019b). In the second part of the diptych, Mosaic Virus, these generated images are displayed on screens that show how the AI-generated tulips evolve. As Ridler explains in her artist statement, the “tulips are controlled by the price of bitcoin”. The aesthetic choice of bringing AI, bitcoin, and tulips together combines the historical speculative bubble created by the 17th-century Tulipmania with contemporary speculative investments in AI cryptocurrencies.
The identified machine vision situation in the database looks like this:
Creator (Artist) is: Classifying, Labeling, Selecting, Speculating
Machine learning, Image generation is: Learning, Generating, Hallucinating, Co-creating
Images are: Classified, Generated
Little of the aesthetics of the artwork remain in the reduced situation entry; however, what is implied is a distribution of cognitive labour between the artist and the machine. The artist creates categories and then interprets photographs classifying the tulips for the datasets. In both Ridler’s classification of tulips and our analysis of this as a machine vision situation, “interpretive flexibility” occurs, despite rigid classification protocols (Feinberg, 2017). Ridler notes that even “something as simple as a tulip is difficult to put into discrete categories – is it white or pale pink, is it orange or yellow”. In turn, the image-generating machine learning model learns to recognise patterns in the 10,000 examples in the dataset, in order to generate new images of tulips.
While human agency is still crucial to create an AI artwork like Mosaic Virus, some cognitive functions like discerning patterns and drawing inferences are externalised to machine vision. In Unthought, N. K. Hayles proposes a definition of cognition that allows us to understand technological objects as cognisers: “Cognition is a process that interprets information within contexts that connect it with meaning” (Hayles, 2017:22). She argues that while consciousness may be attributable primarily to humans, cognition is a capacity that we, according to Hayles (2017), share with other animals, perhaps even plants, as well as with technical devices. Hayles describes computational media, and especially AI, as “quintessentially cognitive technologies” (Hayles, 2017:41) with the ability to process information, identify patterns, and make inferences. When we use these technologies, we effectively enter into what Hayles terms “cognitive assemblages,” which, for the example of a cell phone, would include “relay towers and network infrastructures, including switches, fibre optic cables, and/or wireless routers, as well as other components” (Hayles, 2017:8).
The creation of AI art emerges out of cognitive assemblages involving both human and machine interpretation. However, when AI art was popularised by image generators like DALL-E and Midjourney producing new images from text prompts, it became easy to forget the human labour involved. The outcry from artists whose labour has been scraped into internet-sized datasets to train AI image generators highlights AI’s utter dependence on data created by humans (Benzine, 2022). We can bring the human back into AI creativity through Hayles’ notion of “punctuated agency” that “operates within regimes of uneven activity, longer periods when human agency is crucial, and shorter intervals when the systems are set in motion and proceed on their own without direct human intervention” (Hayles, 2017:32).
In the situation from Anna Ridler’s Mosaic Virus, the concept of “punctuated agency” helps us understand how the cognitive creation of an artwork is distributed between the artist and the machine: To create Mosaic Virus, the artist makes conscious aesthetic decisions when collecting and classifying a dataset, choosing a model, and adjusting parameters. The machine learning model then operates within these parameters. Hayles discusses how human non-conscious processes “feed forward intuitions to conscious awareness” (2017:41); likewise, we can understand the technical non-conscious of machine learning models as generating a type of “technical intuitions” (Kronman, 2020). In Mosaic Virus, technical intuitions are expressed as visual hallucinations of impossible tulips. Technical intuitions are then fed forward to conscious cognition when Ridler curates the generated images as an artwork and when the audiences of Mosaic Virus make sense of what the machines hallucinated. This demonstrates the back-and-forth activity of punctuated agency between human and machinic agents within the same assemblage. The specificity of punctuated agency between the actors is not explicitly registered within the machine vision situation structure. Again, however, the format functions as an entryway or invitation to look closer and uncover the connections and processes behind the activities brought out by the verbs.
While our research captures how creative works represent machine vision, this method also opens for the possibility to address the agencies of users interacting with machine vision in creative works. In the Database of Machine Vision in Art, Games and Narratives, there are several examples of digital games and artworks that have the entity “user” as an agent. Some artworks facilitate some type of interaction between the viewers and machine vision technologies, in those cases we would include the viewer of the artwork as a “user agent” in our situation. Research interested in analysing user interaction would include the user as an agent in each machine vision situation, which shows again how the analysis model is a process of trade-offs between specificity and generalisability.
Tracing agency
The example analyses above demonstrate how the machine vision situations model can help in qualitative analyses of individual works, even though the model was initially designed to support quantitative analysis in the context of the Database of Machine Vision in Art, Games and Narratives. Taking the machine vision situation as the core unit of analysis allowed us to create structured data about the distribution of agency in machine vision interactions in a form that could be analysed quantitatively. We have begun to publish findings from our analyses of this dataset, identifying what the most common technologies are doing in the works in which they are used or represented, and what is most commonly being done to them (Rettberg, 2022b). For instance, drones are most commonly represented as recording, killing, transmitting and targeting and are represented as being controlled by human beings (Rettberg, forthcoming). Based on a machine learning analysis of the machine vision situations dataset, Rettberg (2022a) developed the method of ‘algorithmic failure’ to identify particularly salient cases for further study. The data collected on digital games formed the basis for three studies: one on the use of surveillance cameras as an interface in digital games, proposing the term ‘cyborg vision’ to account for the experience of embodied surveillance that these games offer to the player (Solberg, 2022a); a second on how holograms mediate between human and non-human actors in games (Solberg, 2021); and a third on enhanced vision in games and its relation to ideas of domination and power (Solberg, 2022b). The data collected on artworks formed the basis for Kronman’s (2023) analysis of different approaches to hacking machine vision in art, revealing how art is used to expose bias in machine vision, as well as papers on non-conscious cognition and agency (Kronman, 2020), and aerial perspective and prediction in machine vision assemblages (Kronman, 2019). Gunderson (2021) drew on the database to analyse the representation of augmented reality in popular culture. On the level of the dataset as a whole, the information about each character’s represented gender, race, age, species and sexuality in connection with the assigned verbs can also be used to study racial bias and other biases in how machine vision technologies are imagined across many creative works. The dataset has been deposited in the UiB Open Research Data repository and is available for futher research under a Creative Commons licence (Rettberg et al., 2022a).
The main insight to come out of this cumulative process is just how many different things machine vision technologies can be seen to do when they become involved in events. When we started out, we naively assumed that these technologies’ contribution to unfolding actions would mainly involve actions such as “seeing”, “representing”, or “recording”, but it soon became clear that this was not the case. As shown by the examples above, these visually oriented actions are present, but they are often accompanied by actions that would not initially be ascribed to these types of technologies. Some of these actions have historically been thought of as fundamentally human capacities, such as imagining, interpreting, or deciding. In other cases, the agential contributions of machine vision are inseparable from acts such as killing, creating, or tricking. What a specific technology can be said to do, whether in fiction or reality, can only be determined by attending to the actual situation in which it can be said to make a difference. Although we may imagine that we are in control and that these technologies are mere tools for human intentions, this idea is undermined by our own depictions of them in games, art, and narratives.
While each of our papers grew out of the quantitative work that went into making the database, several have turned to qualitative, close readings of works and situations as their main analytical method (Gunderson, 2021; Kronman, 2020; Kronman, 2023; Solberg, 2021; Solberg, 2022a; Solberg, 2022b). Based on this experience, this paper demonstrates how the concept of machine vision situation, and the formal data structure we developed for it, can serve as a tool to identify the compelling and relevant moments for analysis within a work, and function as a framework for not just quantitative, but also qualitative analysis of distributed agency. As such, it offers the theoretical basis for and a description of the machine vision situation as an analytical model, a model we argue is broadly applicable well beyond studies of machine vision in cultural works.
Although initially designed for the purpose of quantitative analysis across works, in the above three examples, the machine vision situation functions as the entry point for qualitative analysis of human-non-human assemblages. Simply identifying the actors and their doings in a situation produces new insights into how humans, technologies and other entities affect and are affected by each other in everyday interactions. This structure creates a rich foundation for closer analysis, inviting the researcher to trace the connections between the agents, understand the processes behind the verbs, and see what emerges from the situation as a whole. In Drones Don’t Kill People the situations helped us understand the distribution of power between the actors. Furthermore, they brought out the pivotal moments through which something new emerged out of human/non-human assemblages. In Detroit: Become Human, we saw how the agency as represented within the work calls attention to its entanglement with the agency that was not included in the situation – that of the player and the console and the game itself as a technical/digital object. With the artworks Myriad and Mosaic Virus, analysing the machine vision situation reveals how cognition is distributed between human and technical actors in machine learning processes, while also calling attention to what does not fit into the situation structure – the process of punctuated agency between them. In each of these works, the machine vision situation reveals core aspects of the distribution of agency. Furthermore, the situation functions as a provocation, an incitement to look closer at what does not fit the structure, the excess, the complications – and that is often where a more profound understanding is to be found. By following a set structure, messy entanglements that might otherwise have been overlooked call attention to themselves and become obvious. A close reading of one or more related situations allows the researchers to disentangle complex distributions of agency in individual moments when machine vision technologies make a difference, while a large dataset of situations creates the foundations for a quantitative analysis of agency across different contexts. In so doing, the machine vision situation framework straddles the gap between quantitative and qualitative research methods.
While our project collected and analysed data about creative works, we are confident that the machine vision situation can be productively applied to other contexts in which human and non-human actors interact, including everyday encounters with machine vision and the discursive imaginaries through which people make sense of these technologies. Agency is distributed through action, interaction and narrativisation, and we expect that the framework of the machine vision situation can be applied similarly across disciplines and methodological approaches. Although conceptualised through the study of machine vision technologies, there is no reason why this method should be limited to machine vision technologies. As a method, the situation structure should be generalisable to any interaction between human and non-human actors in which agency is distributed in novel ways; hence, further theoretical work could expand it into a more general concept of a “sociotechnical situation”.
By describing what both technologies and humans do in each situation and attaching actions to each of them, we produce information on how they are represented as actors and how actions, or doings, are distributed between them. As a result, we are able to trace the interpretative, meaning-making and communicative contributions of machine vision technologies, as well as their material functioning. This means that we as researchers also constitute an assemblage with the database; the database emerging through the parameters we give it, and the database subsequently directing our actions, interpretations and readings. The concepts, theories and ideas that arise from it cannot be traced back to a single researcher or even be limited to our efforts as a team; the database itself has an active role in their creation. Our thoughts and ideas, and future articles, will contain traces of the effects of this assemblage.
The knowledge we produce is conveyed with and through the structure we created to produce it, but that does not mean that it is contained by it. In the process of creating a quantitative dataset, discussing the structure, reading, playing, interacting with works, and agonising over which verbs to assign, a method emerges and takes on a life of its own. Quantitative data unfolds and reveals itself as a qualitative wellspring.
Ethical approval
Ethical approval and consent were not required.
Author contributions
Gunderson was the lead writer, developed the theoretical framework and wrote most of the paper. Rettberg acquired funding, was the principal investigator for the Machine Vision project, and had the initial idea of analysing agency using a database. Gunderson and Solberg first conceptualised the situations model by combining agents and verbs. Rettberg, Kronman, Gunderson and Solberg collectively developed the analytical model from there in discussion and by analysing works for the database. De Seta joined the project after the database was established and contributed to the database and further development of the ideas. Kronman wrote the section about digital art, Solberg wrote the section about games, Rettberg paragraphs about digital humanities methods and the project overall, and de Seta contributed to the overall framing and conclusion. All authors participated in revisions and editing, with Gunderson coordinating the process.
Acknowledgements
The authors would like to thank Annette Markham, who participated in discussions at the early stages of developing the situations model, and the many contributors to the Machine Vision database, especially Stein Magne Bjørklund, Linn Heidi Stokkedal and Kurdin Jacob. For a full list of contributors to the database, see Rettberg et al. (2022b).
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