PRACTICES FOR A MACHINE CULTURE: A CASE STUDY OF INTEGRATING CULTURAL THEORY AND ARTIFICIAL INTELLIGENCE

In taking into account the contemporary development of technology, the author defends an integration of cultural theory and artificial intelligence research into what she calls cultural informatics. The author reviews the history of artificial intelligence from classical to what is now called alternative artificial intelligence research. A small but active community of researchers focusing on critical technical practices has developed in artificial intelligence research; among other items, they have questioned the traditional understanding of an agent as involved in purely logical and formal relations in order to take into account the embodiment of the agent. ABSTRACT In taking into account the contemporary development of technology, the author defends an integration of cultural theory and artificial intelligence research into what she calls cultural informatics. The author reviews the history of artificial intelligence from classical to what is now called alternative artificial intelligence research. A small but active community of researchers focusing on critical technical practices has developed in artificial intelligence research; among other items, they have questioned the traditional understanding of an agent as involved in purely logical and formal relations in order to take into account the embodiment of the agent.


INTRODUCTION
We are early 21st-century humanity, the inheritors of industrialism, the progenitors of the information age. We live in a machine culture; in our daily lives, we are more and more surrounded by and interfaced with machines. We are no longer, like our ancestors, simply supplied by machines; we live in and through them. From our workplaces to our errands about town to our leisure time at home, human experience is to an unprecedented extent the experience of being interfaced with the machine, of imbibing its logic, of being surrounded by it and seeking it out: pager, cell phone, Palm Pilot, the latest version, the state of the art, the most advanced engineering.
Given that this is our cultural state, one of the most urgent questions we face as a society is the identification of practices which are adequate for intervening in its development. In this intimate machine-culture constellation, how can we decide what we ought to do? How should we as a society spend our resources? What interventions are possible, what are not possible, what are advisable, what had we better stay away from?
One candidate for a practice that could answer these questions is computer science, which has developed extensive practices for constructing computational machinery. Computer scientists understand well how machines can be built, what kinds of technology are possible, and what kinds could be possible if more effort were invested. They are trained to identify shortcomings in technology and to propose solutions to those shortcomings. In practice, they tend to have an intimate familiarity with the inner workings of machines of a sort which is difficult for non-technicalworkers to develop. /p.5-6/ At the same time, computer science suffers a disciplinary amnesia to the machine's cultural context. Computer scientists are trained to focus on machinery, i.e. what can be done, but not on whether it should be done or how it will be applied. The computer remains a black box, within which computer scientists work and outside of whose impermeable boundary the rest of culture and society goes about its business. Questions of sociocultural implication are not answerable within this framework 1 .
Another candidate for a practice to address machine culture is the cultural studies of science, which has developed extensive practices for analyzing technology in a cultural context. Cultural critics know how technology is taken up in and influences broader culture, as well as how cultural background -such as unconsciously held metaphors and philosophies -can encourage the development of certain forms of technology at the expense of others. Cultural critics also have access to tools for analyzing the political and material economies which enable particular forms of technologies and discourage others; they know the cultural pressure points.
At the same time, cultural studies is at a disadvantage in proposing new interventions in machine culture because, as Richard Doyle puts it, it has historically been a consumer of practices rather than a producer of ones. That is to say, cultural studies has the tendency to critique, rather than to generate new practices which /p. 6-7/ 1 The one major exception to this black-boxing is the field of human-computer interaction (HCI), which looks closely at the human context of computing. See for example Brenda Laurel and S. Joy Mountford, The Art of Human-Computer Interface Design, Addison-Wesley, 1990. However, the more socioculturally interesting aspects of HCI generally remain ghettoized there; HCI as a speciality serves as a reason for the nonspecialized to concentrate on other things.
Practices for a Machine Culture (v.1.0A -15/12/00) Phoebe Sengers S U R F A C E S Vol. VIII.107 ƒolio 7 respond to critique. As a result, it often lacks agency in the critiqued practices, being marginalized as a kind of disciplinary Cassandra. In addition, because cultural studies tends not to engage in the practices it criticizes, it frequently lacks the intimate (though not necessarily self-reflective) knowledge of those engaging in those practices, and may at times misunderstand them.
I believe the technical practices of computer science and engineering and the critical practices of cultural studies and the humanities both provide important ingredients to intervene in machine culture, but neither is sufficient alone. In order to be able to address contemporary human experience, we need science and the humanities to be combined into hybrid forms which can address the machinic and the human simultaneously. Squeezed in between the disciplines, we can already see these forms developing.

A CULTURAL INFORMATICS
At the confluence of computation and the humanities, there are already numerous hybrid practices. Computation itself is the object of humanities research: the history of computation, the sociology of computer use, cultural criticism of Artificial Life 2 . /p. 7-8/ Computational tools are used for humanistic projects; humanists compose with word processors, send each other email, read the latest articles over the Web. At the same time, computational artefacts become essential research tools; automatic text analysis is used to support literary criticism, scholarly papers appear in hypertext, collaborative writing environments are used to co-author texts. And in conjunction with the adoption of computational tools, computational concepts are borrowed and adapted to humanist projects: chaos theory as a method of literary analysis, the cyborg as a model of subjectivity, the robot historian as first-person perspective 3 .
These hybrid practices are an essential (and perhaps inevitable) response to machine culture. At the same time, the approaches outlined so far share one disadvantage: an underlying disciplinary split. Computation is seen from the outside, to be observed, analyzed, used, learned from. The development of computational tools, however, remains largely in the domain of computer scientists, to be informed by humanist wishes, to be intrigued by humanist appropriations, to be confused by humanist critique, but to be done using time-honored engineering methodologies.
But in a world where machinery is woven in to the fabric of our daily lives, it is, while useful, not enough to approach computation at an arm's length, to make it the object or pre-given tool /p. 8-9/ of the humanities. The humanities must not only observe, use, and critique computation, but also ingest it. Computing itself must become a humanist discipline.
What this does not mean is the simple use of humanist results in order to optimize computer programming, the development of analytic Shakespeare generators, the reduction of the humanities to what can be output by a computer program. Instead, humanist forms of computing can be a set of practices incorporating a critical, self-reflexive viewpoint into technical work, using the research strategies and values of the humanities, embodying those values and traditions in changing technologies that in turn change human lives. They are oriented towards and respect the full complexity of human experience in the world, rather than reducing that experience to simple rules in the traditions of the natural sciences. They carry a healthy scepticism about the origin and value of computational concepts and tools, but rather than reject them they reorient them. They realize that the term computer science is a historical term, originally used to establish the legitimacy of computing as a coherent and respectable discipline, now artificially limiting the full breadth of possible computational research. This is a cultural informatics. This is what a growing and hybrid group of artists, researchers, and critics already do.
Humanists have called for such successor science projects for years. The research tradition of a humanist computation, though somewhat buried under the overwhelming mass of traditional computer science, already exists and is gaining strength. It is generally unnoticed because humanistically-informed computing is still computing. It is specific, oriented towards a mostly scientific academic subculture, flying below the radar screens of the humanities. /p. 9-10/ In this paper, I will also work specifically, looking at the confluence of cultural studies and Artificial Intelligence (AI). I will focus particularly on the subfield of autonomous agents, artificial creatures that 'live' in physical or virtual environments, capable of engaging in complex action without human control. While giving an overview of research in this field, I will explain how issues of subjectivity unconsciously arise, suggesting an entrypoint for cultural studies. I will lay out how cultural studies and agent research can be and are being synthesized, and look at the mostly unknown research tradition that already exists in this area. I will then connect the critical practices within AI to those in computer science and general, as well as complementary approaches to cultural informatics emerging from within the arts.

Introduction to Autonomous Agents
One of the dreams of AI is the construction of autonomous agents, independent artificial beings. Rather than slavishly following our orders, or filling some tiny niche of activity that requires some aspect of intelligence (for example, playing chess), these artificial creatures would lead their own existences, have their own thoughts, hopes, and feelings, and generally be independent beings just as people or animals are. Autonomous agents would be more than useful machinery, they would be independent subjects.
This AI dream of mechanical creatures that are, in some sense, alive, can seem bizarre at first glance. It is therefore important to note that this is not an idea that is new in AI, but, as Simon Penny notes, the continuation of a tradition of anthropomor-/p. 10-11/ phization that extends back thousands of years 4 . In this sense, the AI dream is similar to the 'writing dream' of characters that ring true, to the 'painting dream' of images that seem to step out of the canvas, to the fantasies of children that their teddy bears are alive, and to many other Pygmalionesque dreams of human creations that begin to lead their own lives.
But there is certainly a sense in which AI brings a new twist to these old traditions. AI as a cultural drive needs to be seen in the context of post-industrial life, in which we are constantly surrounded by, interfaced with, and defined through machines. At its worst, AI adds a layer of seductive familiarity to that machinery, sucking us into a mythology of user-friendliness and humanity while the same drives of efficiency, predictability, quantifiability, and control lurk just beneath our perception.
But at its best, AI invokes a hope that is recognizable to humanists -that is invoked, in fact, by Donna Haraway in her « Cyborg Manifesto'' 5 . This is the hope that, now that we are seemingly inescapably surrounded by technology, this technology can itself become hybridized and develop a human face. This version of the AI dream is not about the mechanistic and optimized reproduction of living creatures, but about the becoming-living of machines. The hope is that rather than forcing humans to interface with machines, those machines may learn to interface with us, to present themselves in such a way that they do not drain us of our humanity, but instead themselves become humanized. /p. 11-12/ In the 1950's and early 1960's, this dream for AI, for good and for bad, was embodied in cybernetics. W. Grey Walter, for example, built small robots with rudimentary "agenty'' behaviors 6 . He called his robots 'turtles;' they would roam around their environment, seeking light, finding food, and avoiding running into things. Later models could do some rudimentary associative learning.
But as cybernetics fell out of fashion, AI research began to focus more on the cognitive abilities an artificial agent might need to have higher-level intelligence, and less on building small, complete (if not so smart) robots. At least partially because the task of reproducing a complete creature has been so daunting, AI spent quite a few years focused on building individual intelligent capabilities, such as machine learning, speech recognition, story generation, and computer vision. The hope was that, once these capabilities were generated, they could be combined into a complete agent; the actual construction of these agents was often indefinitely deferred.
More recently, however, the field of autonomous agents has been enjoying a renaissance. The area of autonomous agents focuses on the development of programs that more closely approach representations of a complete person or creature. These agents are programs which engage in complex activity without the intervention of another program or person. Agents may be, for example, scientific simulations of living creatures, characters in an interactive story, robots who can independently explore their environment, or virtual 'tour guides' that accompany users on their travels on the World Wide Web 7 . From the early debacles /p. 12- While these applications vary wildly, they share the idea that the program that underlies them is like a living creature in some important ways. Often these ways include being able to perceive and act on their (perhaps virtual) environment; being autonomous means they can make decisions about what to do based on what is happening around them and without necessarily consulting a human for help. Agents are also often imputed with rationality, which is defined as setting goals for themselves and achieving them reasonably consistently in a complex and perhaps hostile environment.

Agent as Metaphor
The definition of what exactly is and is not an agent has at times been the source of vehement controversy in the field. Mostly these controversies revolve around the fact that any strictly /p. formal definition of agenthood tends to leave out such wellbeloved agents as cats or insects, or include such items as toasters or thermometers that a lay person would be hard-pressed to call an agent. With some of the looser definitions of agents, for which the word 'agent' just seems to be a trendy word for 'program,' skeptics can be forgiven for wondering why we are using this term at all.
Here, I will take agenthood broadly to be a sometimes-useful way to frame inquiry into the technology we create. Specifically, agenthood is a metaphor we apply to computational entities we build when we wish to think of them in ways similar to the ways we understand living creatures. Calling a program an agent means the program's designer or the people who use it find it helpful or important or attractive to funders to think of the program as an independent and semi-intelligent coherent being. For example, when we think of our programs as agents we focus our design attention on 'agenty' attributes we would like the program to have : the program may be self-contained ; it may be situated in a specific, local environment ; it may engage in 'social' interactions with other programs or people 8 . When a program is presented to its user as an agent, we are encouraging the user to think of it not as a complex human-created mechanism but as a user-friendly, intelligent creature. If 'actually' some kind of tool, the creature is portrayed as fulfilling its tool-y functions by being willing to do the user's bidding 9 . Using the metaphor 'agent' for these applications lets us apply ideas about what living agents such as dogs, beetles, or bus drivers are like to the design and use of artificially-created programs. /p. 14-15/ S U R F A C E S Vol. VIII.107 ƒolio 15

Agenthood in Classical and Alternative AI
But not all AI researchers agree on which conceptions of living agents are appropriate or useful for artificial agents. The past 15 years in particular have seen an at times spectacular debate between different strains of thought about the proper model of agent to use for AI research 10 . Rodney Brooks, for example, distinguishes between 'symbolically-grounded' and 'physicallygrounded' agents 11 . These symbolically-grounded agents spent most of their time in abstract cogitation; their programs manipulate representations of the "real world" (for example, in database form), but rarely come into contact with that real world. Physicallygrounded agents, on the other hand, manipulate and react to the environment itself without having external objects explicitly represented in their program code.
Philip Agre and David Chapman distinguish agents using 'plans-asprograms' from agents using 'plans-as-communication.' This is a distinction based on the relative importance of internallydetermined planned-out activity versus a more improvised, moment-by-moment immersion in environmental circumstances. Agents that use plans as programs are heavily invested in their internal representation of action; they engage in abstract, hierarchical planning of activity before engaging in it (often including formal proofs that the plan will fulfill the goal the agent is given). Agents that use plans as communication see plans as a convenience but not a necessity. They are designed to take advantage of an action loop with respect to their /p. environment and may only refer to plans as ways to structure common activities 12 .
Another common distinction is between situated and cognitive agents. Situated agents are thought of as embedded within an environment, and hence highly influenced by their situation and physical make-up. Cognitive agents, on the other hand, engage in most of their activity at an abstract level and without reference to their concrete situation.
Each of these distinctions is not independent of the others. When looking at such classification attempts at a whole, a distinct theme emerges. AI research in general can be understood as involving two major trends in thinking: a main stream often termed classical AI (also known as Good For AI researchers, the term classical AI refers to a class of representational, disembodied, cognitive agents, based on a model that proposes, for example, that agents are or should be fully rational and that physical bodies are not fundamentally pertinent to intelligence. The more extreme instances of this type of agent had their heyday in the 60's and 70's, under a heady aura of enthusiasm that the paradigms of logic and problem-solving might quickly lead to true AI. One of the earliest examples of this branch of AI is Allen Newell and Herbert Simon's GPS, the somewhat optimistically titled "general problem solver.'' This program proceeds logically and systematically from the statement of a mathematical-style puzzle to its solution 14 . Arthur Samuel's checker player, one of the first programs that learns, attempts to imitate intelligent game-playing by learning a polynomial function to map aspects of the current board state to the best possible next move 15 . Terry Winograd's SHRDLU maintains a simple representation of blocks lying on a table, and uses a relatively straightforward algorithm to accept simple natural language commands to move the virtual blocks 16 . While the creators of these programs often had more subtle understandings of the nature of intelligence, the programs themselves reflect a hope that simple, logical rules might underlie all intelligent behavior, and that if we could discover those rules we might soon achieve the goal of having intelligent machinery. /p.17-18/ But the classical model, while allowing programs to succeed in many artificial domains which humans find difficult, such as chess, unexpectedly failed to produce many behaviors humans find easy, such as vision, navigation, and routine behavior. The recognition of these failures has led to a number of responses in the 80's and 90's. Some researchers -most notably Winograd, who wrote an influential book with Fernando Flores on the subject 17 -have decided that the intellectual heritage of AI is so bankrupt they have no choice but to leave the field. By far the majority of AI researchers have remained in a tradition that continues to inherit its major research framework from classical AI, while expanding its focus to try to incorporate traditionally neglected problems (we might call this 'neo-classical AI'). A smaller but noisy group has split from classical AI, claiming that the idea of agents that classical AI tries to promote is fundamentally wrong-headed.
These researchers, who we will here call alternative AI, generally believe that the vision of disembodied, problem-solving minds that explicitly or implicitly underlies classical AI research is misguided. Alternative AI focuses instead on a vision of agents as most fundamentally nonrepresentational, reactive, and situated. Alternative AI, as a rubric, states that agents are situated within an environment, that their self-knowledge is severely limited, and that their bodies are an important part of their cognition.

Agent Technology as Theory of Subjectivity
The dialogue and debate between these two types of agents is not only about a methodology of agent-building. An underlying source of conflict is about which aspects of being human /p. are most essential to reproduce. Classicists do not deny that humans are embodied, but the classical technological tradition tends to work on the presupposition that problem-solving rationality is one of the most fundamental defining characteristic of intelligence, and that other aspects of intelligence are subsidiary to this one. Likewise, alternativists do not deny that humans can solve problems and think logically, but the technology they build is based on the assumption that intelligence is inherent in the body of an agent and its interactions with the world; in this view, human life includes problem-solving, but is not a problem to be solved.
It is in these aspects of AI technology -ones that are influenced by and in turn influence the more philosophical perspectives of AI researchers -that we can uncover, not just the technology of agents, but also theories of agenthood. 20/ is based on a model of subjectivity as essentially representational, rational, and disembodied. Alternative AI technology presupposes that it is essentially reactive, situated, and embodied.
These two categories can be clearly seen within AI research.
Within that research community, they are generally seen as arising from certain tensions in technical practice itself. But these categories should be familiar to cultural theorists from a quite different context; they directly correspond to rational (or Enlightenment) and schizophrenic (or postmodern) subjectivity 18 .
Rational subjectivity is based on the Cartesian focus on logical thought: the mind is seen as separated from the body, it is or should be fundamentally rational, and cognition divorced from emotion is the important part of experience. This model has overarching similarities with, for instance, Allen Newell's theory of Soar, which describes an architecture for agents that grow in knowledge through inner rational argumentation 19  The development of the notion of schizophrenic subjectivity is based on perceived inadequacies in the rational model, and is influenced by but by no means identical to the psychiatric notion of schizophrenia. While rational subjectivity presupposes that people are fundamentally or optimally independent rational agents with only tenuous links to their physicality, schizophrenic subjectivity sees people as fundamentally social, emotional, and bodily. It considers people to be immersed in and to some extent defined by their situation, the mind and the body to be inescapably interlinked, and the experience of being a person to consist of a number of conflicting drives that work with and against each other to generate behavior. In AI, this form of subjectivity is reflected in Brooks's subsumption architecture, in which an agent's behavior emerges from the conflicting demands of a number of loosely coupled internal systems, each of which attempts to control certain aspects of the agent's body based almost entirely on external perception rather than on internal cogitation 21 .
Each class of agent architectures closely parallels a kind of subjectivity. Just as alternative AI has arisen in an attempt to address flaws in classical AI, the concept of schizophrenic subjectivity has arisen in response to perceived flaws in the rational model's ability to address the structure of contemporary experience.

CULTURAL STUDIES AND AI IN THE AGE OF THE SCIENCE WARS
Certainly cultural studies has not turned a blind eye to the ascendancy of science and technology in contemporary culture. The last 15 or 20 years has seen an explosion of research analyzing the complex relationships between science and the rest of culture. This, at least theoretically, lays the groundwork for a potential collaboration between science studies and science. Science studies, after all, examines culturally-based metaphors that inform scientific work, and thereby often uncovers deeply-held but unstated assumptions that underlie it. Scientists are also generally interested in understanding the forces, both conscious and unconscious, that can shape their results. If there are ways in which they can better understand the phenomena they study or build the technology they want to create, they are all ears. In this respect, as Evelyn Fox Keller points out, the insights of science studies can contribute great value to science's self-understanding 22 .
At the same time, many practitioners of science studies are deeply interested in science as it is actually practiced on a day-to-day level. This means scientists, with their in-depth personal experience of what it means to do scientific work, are privy to perspectives that can enrich the work of their science studies counterparts. Science studies simply is not possible without science, and an important component of it is an accurate reflection of the experiences of scientists themselves.

A Siege Mentality
With all the advantages that cooperation could bring, you might think that science and science studies would be enthusiastic partners on the road to a shared intellectual enterprise. Alas, the practitioners of science studies and many of their hapless subjects know that that is far from the case. Productive exchanges between cultural critics and scientists interested in the roots of their work are hampered by the disciplinary divide between them. This divide blocks cultural critics from access to a complete understanding of the process and experience of doing science, which can /p. 23-24/ degrade the quality of their analyses and may lead them to misinterpret scientific practices. At the same time, scientists have difficulty understanding the context and mindset of critiques of their work, making them unlikely to consider such critiques seriously or realize their value for their work, potentially even leading them to dismiss all humanistic critiques of science as fundamentally misguided 23 .
This feedback loop of mutual misunderstanding has grown into a new tradition of mutual kvetching. Cultural critics may complain that scientists unconsciously reproduce their own values in their work and then proclaim them as eternal truth. They may feel that scientists are not open to criticism because they want to protect their high (relative to the humanities') status in society.
Simultaneously, scientists sometimes complain that cultural critics are absolute nihilists who do not believe in reality and equate science with superstition 24 . They fear that cultural critics undermine any right that science has as a source of knowledge production to higher status than, say, advertising. Finally, both sides complain incessantly -and correctly -of being cited, and then judged, out of context.
The unfortunate result of this situation is a growing polarization of the two sides. In the Science Wars, pockets of fascinating interdisciplinary exchanges and intellectually illuminating debate are sadly overwhelmed by an overall lack of mutual understanding and accompanying decline of goodwill. While most par-/p.24-25/ ticipants on both sides of the divide are fundamentally reasonable, communication between them is impaired when both sides feel misunderstood and under attack. This siege mentality not only undermines the possibility for productive cooperation ; with unfortunate frequency, it goes as far as cross-fired accusations of intellectual bankruptcy in academic and popular press and nasty political battles over tenure. These unpleasant incidents not only help no one but also obscure the fact that both the academic sciences and the humanities are facing crises of funding in an economy that values quick profit and immediate reward over a long-term investment in knowledge. In the end, neither science nor science studies benefits from a situation best summed up from both sides by Alan Sokal's complaint : « The targets of my critique have by now become a self-perpetuating academic subculture that typically ignores (or disdains) reasoned criticism from the outside'' 25 .

Science Wars, AI Skirmishes
While most scientists remain blissfully unaware of the Science Wars, they are not unaffected by them. Within AI, the tension between the self-proclaimed defenders of scientific greatness and the self-identified opponents of scientific chauvinism is worked out under the This can be seen most clearly in a rather unusual opinion piece that appeared several years ago in the AI Magazine 26 . The remarkable rhetoric of this essay in a journal more often devoted to the intricacies of extracting commercially relevant information from databases may be appreciated in this excerpt: Once upon a time there were two happy and healthy babies. We will call them Representation Baby (closely related to Mind Baby and Person Baby) and Science Baby (closely related to Reality Baby).
These babies were so charming and inspirational that for a long time their nannies cared for them very well indeed. During this period it was generally the case that ignorance was pushed back and human dignity increased. Nannies used honest, traditional methods of baby care which had evolved during the years. Like many wise old folk, they were not always able to articulate good justifications for their methods, but they worked, and the healthy, happy babies were well growing and having lots of fun. the babies are in danger from their zealous ways. We will focus on two nannies who seem to be close friends and often can be seen together -Situated Nanny (called SitNanny for short) and Radical Social Constructivist Nanny (known to her friends as RadNanny) (15) 27 .
A little decoding is in order for those not intimately aware of both the AI debates and the Science Wars. "SitNanny'' represents situated action, a brand of alternative AI that focuses its attention on the way in which agents are intimately related to, and cannot be understood without, their environment. "RadNanny,'' as is immediately clear to even the most naive science studies aficionado, is the embodiment of the cultural studies of science, social constructivism being the belief that science, like every other human endeavor, is at least partially a product of sociocultural forces (the 'radical' here functions as little more than an insult, but implies that science is purely social, i.e. has absolutely no relationship to any outside reality).
Having broken the code, the implication of this excerpt is clear: everything in AI was going fine as long as we thought about things in terms of science and knowledge representation, one of the core terms of classical AI. Of course, this science was not always wellthought-out, but it was fundamentally good. That is, until that dastardly alternative AI came along with cultural studies in its tow and threatened nothing less than to kill the babies.
/p. Since the majority of their audience presumably has little awareness of science studies, the authors are happy to do their part for interdisciplinary awareness by explaining what it is. They state, in a particularly nice allusion to 1950's anti-Communist hysteria, that science studies aims at nothing less than to "reject the entire fabric of Western science'' (15). Science studies, we are informed, believes "that all science is arbitrary and that reality is merely a construction of a social game'' (23). In the delightful tradition of the Science Wars, several quotations are taken out of context to prove that cultural critics of science believe that science is merely an expendable myth.
/p. 28-29/ 28 One must presume that the authors were aware of this and did their best to raise cultural critics' hackles. The statements Hayes et al. make are simply inaccurate descriptions of science studies. In reality, science studies tends to be agnostic on such questions as the arbitrariness of science and on the nature of reality, to which science studies generally does not claim to have any more access than science does. When science studies does look into these issues it does so in a much more subtle and complex way than simply rejecting or accepting them.
But what is more important than these factual inaccuracies is that the article promotes the worst aspects of the Science Wars, since the very tone of the article is chosen to preclude the possibility of productive discussion. Science studies is simply dismissed as ludicrous. If uninformed scientists reading the article have not by the end concluded that science studies is an evil force allied against them, with alternative AI its unfortunate dupe, it is certainly not for lack of trying

AI IN CULTURE, AI AS CULTURE
But is it really true that science studies is an enemy of AI? After all, no one disputes that AI is, among other things, a social endeavor. Its researchers are undeniably human beings who are deeply embedded in and influenced by the social traditions in which they consciously or unconsciously take part, including but by no means limited to the social traditions of AI itself. It seems that taking these facts seriously might not necessarily damage AI, but could even help AI researchers do their work better.
In this section, we will buck the trend of mutual disciplinary antagonism by exploring the potential of what /p. 29-30/ critics (the question of why those scientists would find it interesting or even fruitful to keep such unseemly company is left unanswered). And in an exhaustive survey of every important figure in cultural studies, some of the most influential 'culturalist scientists' are left out altogether. A glaring omission is Richard Lewontin, whose influential books on the cultural aspects of biology are the sidelight to an illustrious career as a geneticist 31 .
Similarly, the hypothesis that scientists do not know or care about the effects of their work is contradicted by the work of Martha Crouch 32 . Crouch is a botanist who, after many years of research, noticed that the funding of botany combined in practice with the naive faith of scientists in their own field to completely undermine the idealistic goals of plant scientists themselves. Crouch determined to help scientists such as herself achieve their own stated goals of, for example, feeding the hungry, by adding to their self-understanding through the integration of cultural studies with botany.
But, to be fair, much of the work integrating science with science studies may be invisible to both cultural critics themselves and the scientists whose form of intellectual output seems to largely be attacks on those on the other side of the great intellectual /p. 31-32/ divide. This is because scientists who are actually using culturalist perspectives in their work generally address that work to their scientific subcommunity, rather than to all of science and science studies as a whole. And in work that is addressed to a technical subfield, it is usually not particularly advantageous to mention that one's ideas stem from the humanities, particularly if they come from such unseemly company as hermeneutics, feminism or Marxism.
Here, we will uncover the history of the use of culturalist perspectives within AI as a part of technical work. It turns out that within AI, the use of cultural studies perspectives is not just a couple of freak accidents traceable to a few lone geniuses and / or lunatics. Rather, there is a healthy if somewhat hidden tradition of a number of generations of AI researchers who have drawn inspiration from the humanities in ways that have had substantial impact on the field as a whole. We look at both how cultural studies was found to be useful, and the concrete methods various researchers have used to combine the fields.

Winograd and Flores
Terry Winograd is one of the first and certainly one of the most notorious in his usage of critical theory to analyze AI from the AI researcher's point of view. As mentioned in the review of classical AI, Winograd was a well-known researcher into the machine generation of human language. In Understanding Computers and Cognition, Winograd and Flores analyze AI as a continuation of the analytic tradition 33 . AI's investment in this tradition, they conclude, is so great that it cannot address what they consider to be fundamental attributes of intelligence. Their critique is based on the Heideggerian notion that people approach the world from a set of prejudices that cannot be finitely articulated. If these prejudices cannot be finitely articulated, then they cannot be explicitly represented in machinery; any machinic representation of subjectivity will therefore necessarily leave out some of the complex background knowledge with which people approach real-world situations. This means that AI is able to solve limited, formal problems, but cannot attain true intelligence because "[t]he essence of intelligence is to act appropriately when there is no simple pre-definition of the problem or the space of states in which to search for a solution'' (98). Winograd and Flores argue that instead of making computers that can communicate with us, we should make computers a means to aid communication between people.
While Winograd and Flores's arguments certainly made a splash in the field, it must be honestly stated that they probably did not cause too many scientists to leave AI (and they were not intended to). The basic flaw from this perspective in the argument is that it forces AI researchers to choose between believing in Heidegger and believing in AI. One can hardly blame them if they stay with the known evil.
What is interesting to those who remain in AI, however, is Winograd and Flores's methodology for combining a critical perspective with AI. Winograd and Flores analyze the limitations of AI that stem from its day-to-day methodologies. /p. When they find those constraints to exclude the possibility of truly intelligent behavior, they decide instead to start building systems in which those constraints become strengths. In other words, they decide that artificial systems necessarily have certain characteristics of rigidity and literalness, then ask themselves what sorts of social situations could be aided by a rigid, literal system. They then build a system that is an enforcer of social contracts in certain, limited situations where they feel it is important that social agreements be clearly delineated and agreed upon. Specifically, the system articulates social agreements within work settings, so that workers are aware of who has agreed to do what. This new system is designed to be useful precisely because of the things that were previously limitations. Winograd and Flores, then, use cultural studies to inform technical development by finding constraints in its methodologies, and then using those constraints so that they become strengths.

Suchman
Lucy Suchman is an anthropologist who, for a time, studied AI researchers and, in particular, the ideas of 'planning' 34 . Planning is an area of AI that is, at its most broad, devoted to deciding what to do. Since this broad conception does not really help you sink your teeth into the problem, a more limited notion has been generally used in AI. This concept of planning is a type of problem-solving where an agent is given a goal to achieve in the world, and tries to imagine a set of actions that can achieve that goal, generally by using formal logic. /p. deeply influenced by ethnomethodology, particularly Garfinkel and Suchman's work described above. Chapman and Agre reject the idea that problem-solving is central to agenthood, and instead see agenthood as process, engaging in a rich set of interactions with other agents and the physical world.
The world of everyday life... is not a problem or a series of problems. Acting in the world is an ongoing process conducted in an evolving web of opportunities to engage in various activities and contingencies that arise in the course of doing so... The futility of trying to control the world is, we think, reflected in the growing complexity of plan executives. Perhaps it is better to view an agent as participating in the flow of events. An embodied agent must lead a life, not solve problems 37 .
This re-understanding of the notion of agent has been an important intellectual strand in alternative AI's reconceptualization of agent subjectivity.
In recent work, Agre has distilled his approach to combining philosophy, critical perspectives, and concrete technical work into an articulated methodology for critical technical practices per se. aside your experience as a being in the world. Instead, that experience should be connected to and affirmed in your work. In this way, they connect with cultural critics of science like Donna Haraway and cultural theorists like Gilles Deleuze and Félix Guattari, who stress the importance of personal experience as a component of disciplinary knowledge 42 .
One of the tensions that has to be resolved in any work that combines science with non-scientific disciplines (of which Buddhism is certainly one !) is the differential valuation of objectivity. Science tends to see itself as objective, generating knowledge that is independent of anyone's individual, personal experiences. Since Varela, Thompson and Rosch want to connect cognitive science as science with individual human experience, they confront this problem of subjectivity versus objectivity headon.
Interestingly, they do this by redefining what objectivity means with respect to subjective experiences. You cannot truly claim to be objective, they say, if you ignore your most obvious evidence of some phenomenon, i.e. your personal experience of it. This is particularly true when one is studying cognition -in this frame of thought, any self-respecting study of the mind should be capable of addressing the experience of having one ! /p. 41-42/ Given that one of the things cognitive scientists (and, by extension, AI researchers) are or should be interested in is subjective experience, Varela, Thompson, and Rosch abandon the focus on objectivity per se. But they stress that this does not lead to the nihilistic abandonment of any kind of judgments of knowledge which seems to haunt the nightmares of many participants in the Science Wars. Rather, they argue that Buddhist traditions have disciplined ways of thinking about that experience. The problem, they say, is not with subjectivity, but with being undisciplined. The goal, then, is being able to generate a kind of cognitive science that is subjective without being arbitrary.

Summary: Perspectives on Integrating AI and the Humanities
Generally, each of these researchers is interested in AI because of a fascination with the nature of human experience in the world. This interest naturally leads them to the humanities, which have dealt with questions of subjective human experience for hundreds of years. These researchers have found various ways to integrate this humanist experience with the science and engineering practices of AI. With respect to the issue of integrating AI and cultural studies that is pursued here, we can sum up their perspectives as follows: • Winograd and Flores contrast existentialist philosophy with the analytic, rationalist philosophy that underlies much AI research. They use the differences between these approaches to understand the constraints that are inherent in AI methodology. existence. They introduce, flesh out, and defend the idea to scientists that subjective does not necessarily mean arbitrary.
While each of these researchers went a different path in integrating cultural studies and AI, often with quite different goals and selfunderstandings, their approaches share common themes. They are based on the idea that humanist conceptions have concrete implications for technology, and that technology can and should be changed to reflect humanist convictions and values. Their work is not a simple incorporation of cultural studies to technical ends, but also re-form both technology and the technical research process in order to align them better with a cultural studies perspective. Technical practices and cultural studies meet as equals.

CRITICAL TECHNICAL PRACTICES IN AI TODAY
In recent years, a small but active community of researchers focusing on critical technical practices has developed in AI. Researchers draw on various strands of cultural studies and cultural critique as practiced in the art community. They share a commitment to philosophical and cultural critique of technology, and its embodiment in new technical systems, which are presented to the computer science community. Three examples give an overview; they are by no means exhaustive.

Penny
Simon Penny's approach to critical technical practices, which he terms "reflexive engineering," integrates a practice of art with robotics. Penny particular and computer science in general. In his work, Penny explores the aesthetics of behavior, i.e. a new aesthetics of interactivity made possible by computational and robotic machinery. Because he is an artist, he argues he is able to more freely explore possible technologies than computer scientists, who are generally constrained to generate functionally oriented, clean, and optimized systems 43 .
Petit Mal, for example, is a minimalistically engineered, whimsical, elegantly clumsy robot, which interacts physically with the audience and whose chaotic behavior elicits an enormous range of culturally-specific interpretations from its audience. The tenuous relationship between Petit Mal's simple design and the audience's complex interpretation points out the extent to which our perceptions of and judgments about technical artefacts are always already embedded in a cultural environment. "Petit Mal constitutes an Embodied Cultural Agent: an agent whose function /p. 45 is self reflexive, to engage the public in a consideration of the nature of agency itself" 44 .

Sack
Warren Sack works in computational linguistics, or the computer analysis of human language use. Using a cultural studies perspective on language leads Sack to choose unusual problems to work on. For example, most story-understanding systems attempt to extract an objective meaning from a giving piece of text. In contrast, Sack has built a system which understands ideological bias of news story by analyzing the roles the various actors in the story play. In his most recent work, Sack has created a tool, the Conversation Map, for analyzing the large-scale conversations that take place in netnews groups, including analysis of the topics of conversation, the ways in which terms are commonly used and related, and the social networks that are built in the course of conversation. Sack's goal in building this system is to be able to understand experimentally how net-based communities and subjectivities develop 45  It seems to me no wonder that, if consciousness and the experience of being alive are left out of the methods of AI, the agents we build based on these methods tend to come across as shallow, stimulusresponse automatons.
In the reduction of subjective experience to mechanistic explanations, AI is by no means alone. AI is part of a broader set of Western cultural traditions, such as positivist psychiatry and scientific management, which tend to devalue deep, psychological, individual, and subjective explanations in favor of broad, shallow, general, and empirically verifiable models of the human. I do not deny that these theories have their use ; but I fear that, if taken as the only model for truth, they leave out important parts of human experience that should not be neglected. I take this as a moral stance, but you do not need to accept this position to see and worry about the symptom of their neglect in AI : the development of agents that are debilitatingly handicapped by what could reasonably accurately, if metaphorically, be termed autism. This belief that science should be understood as one knowledge tradition among others does not imply the rejection of science ; it merely places science in the context of other, potentially -but not always actually -equally valid ways of knowing. In fact, many if not most scientists themselves understand that science cannot provide all the answers to questions that are important to human beings. This means that, as long as AI attempts to remain purely scientific, it may be leaving out things that are essential to being human.
In Because of the great differences between AI and cultural studies, it is inevitable that a synthesis of them will include things unfamiliar to each discipline, and leaves out things that each discipline values.
In my approach to this synthesis, I have tried to select what is to be removed and what is to be retained by maintaining two basic principles, one from AI and one from cultural studies: (1) faith in the basic value of concrete technical implementation in complementing more philosophical work, including the belief that the constraints of implementation can reveal knowledge that is difficult to derive from abstract thought; (2) respect for the complexity and richness of human and animal existence in the world, which all of our limited, human ways of knowing, both rational and nonrational, both technical and intuitive, cannot exhaust.

The Anti-Boxological Manifesto
The methodologies I use inherit many aspects from the research traditions described above. Following Winograd and Flores, I analyze the constraints that AI imposes upon itself through its use of analytic methodologies. Following Suchman, I uncover metaphors that inform current technology, and search for new metaphors that can fundamentally alter that technology. Following Chapman, I provide not just a particular technology of AI but a way of thinking about how AI can be done. Following Agre, I pursue technical and philosophical arguments as two sides of a single coin, finding that each side can inform and improve the other.
The additions I make to these approaches are based on a broad analysis of attempts to limit or circumscribe human experience. I believe that the major way in which AI and similar /p. 51-52/ Concretely, some of my most recent technical work is based on a tracing out and treating of the consequences of the boxological approach current in AI. I argue that the desire to construct agents in terms of a limited number of independent black boxes leads to a form of schizophrenia, or gradual incoherence in the overall behavior of the agent as more and more of these "black boxes" are combined. This schizophrenia can be traced to atomizing methodologies AI inherits from its roots in industrial culture. The disintegration AI researchers can recognize in their agents, like that felt by the assembly line worker and institutionalized mental patient, is at least in part a result of reducing subjective experience to objective atoms, each taken out of context and therefore out of relationship to one another and to the context of research itself.
This suggests that the problems of schizophrenia can be mitigated by putting the agent back into its sociocultural context, understanding its behavior as implicated in a cycle of human interpretation, on the part of both its builder and those who interact with and judge it. This approach to AI, which sees agents not in a sociocultural vacuum but as a form of communication /p. 53-54/ between human beings, I term "socially situated AI" and is closely related to Mateas's Expressive AI. With this metaphor as a basis, it becomes clear that creating coherence means integrating, not the agent's internally defined code, but the way in which the agent presents itself to human users. This changes the focus in agentbuilding from primarily a design of the agent alone, with its subsequent interpretation as an afterthought, to including the agent's comprehensibility in the design and construction of agents from the start.
Narrative psychology suggests that agents will be maximally comprehensible as intentional beings if they are structured to provide cues for narrative. I therefore argue that agent behavior should be structured as narrative, in order to make it as easy as possible for users to make coherent sense of agent activity. I implement this narrative structure for behavior using an agent architecture, the Expressivator, that connects formerly disparate behavior into coherent narrative sequences 52 .
Why should a humanist care about this development? On the basis of my experience, I believe there are several advantages to using cultural studies as a basis for a practice of AI. The first is that by actually practicing AI, the cultural critic has access to a kind of experiential knowledge of science that is difficult to get otherwise and will deepen his or her theoretical analysis. This /p. 54-55/ increased knowledge is expressed in two ways in my work: (1) analysis of alternative AI as a manifestation of industrial culture, and (2) analysis of the metaphorical basis of alternative AI even into the details of the technology. The second advantage is that working within AI allows cultural theorists to not only criticize its workings, but to actually see changes made in practice on the basis of those criticisms. The Expressivator reflects the cultural studies analysis in the fundamental changes it makes in how an agent is conceived and structured. This brings home at a technical level the idea that agents are not simply beings that exist independently, but have authors and audiences by which and for which they are constructed.
Finally, the most important advantage to such an approach is the potential alteration to the rhetoric of mutual assured destruction that currently seems to be prevalent in interdisciplinary exchanges between cultural studies and science. The most fundamental contribution my work tries to make toward a cease-fire in the Science Wars is in demonstrating that 'science criticism' is relevant to and can be embodied in the development of technology, so that there are grounds for the two sides to respect each other, as well as a reason for them to talk. In order to address contemporary experience, we need both sides. My hope is that my work can join other similarly motivated work on whatever side of the interdisciplinary divide to replace the Science Wars with the Science Debates, a sometimes contentious and always invigorating medley of humanist, scientific, and hybrid voices. the climate for this work has dramatically improved. What was once a few lone voices crying out in the wilderness of AI has evolved into a small research community. At the recent Narrative Intelligence Symposium 53 , critical technical practices seemed to have moved into the mainstream of AI; discussion of the details of story-generation systems flowed smoothly into analyses of narrative's function in the formation of subjectivity and the role of AI narrative systems in reinforcing or undermining dominant ideologies.

FIRST AI, THEN THE WORLD?: THE FUTURE OF CRITICAL TECHNICAL PRACTICES
But there is no reason why critical technical practices -practices of technology-building which include a critical perspectiveshould be limited to the subfield of AI. In fact, complementary practices have already developed and continue developing in other parts of computer science. These critical perspectives have long played a role in the field of computer-human interaction, for instance. A nice example is Kristina Höök's work, in which she develops new tools for evaluation that analyze the pleasurable quality of the experience the system provides, rather than focusing on its efficiency 54 .
In a related vein, critical technical practices, and particularly cultural informatics, may have an enormous advantage in developing poetic technology, technical applications which enrich human life, not by making it more efficient, but by inspiring sensations of magic and wonder. Chris Dodge's "The /p. 56-57/ Bed" is a beautiful example of this kind of technology: it is an environment to allow intimate connection between people who are far from one another. A pillow on the bed heats when the remote participant is there, and vibrates in time with the remote person's heartbeat; a curtain moves in time with his or her breath, and colorful shadows are projected onto it according to the tenor of conversation. The result is a feeling of connection and intimacy, made possible not by optimized functionality but by the emotionally-laden overtones of the meaning of bed, light/dark, shadows, and so on 55 .
Certainly, there are still gaps in the work that has been done; in particularly, in AI there has been a heavy emphasis on semiotic, philosophical, and metaphorical analysis, which can be relatively easily "smuggled into" the rhetoric of computing, with a corresponding lack of materialist analysis and work in the political economy of computing. In addition, research in critical technical practices and cultural informatics is generally done under-thetable; research communities are organized by technical application area, not by degree of incorporation of extra-disciplinary viewpoints. If research in this area is to blossom, we will probably need our own mailing lists, workshops, conferences, journals. Coherence of the community may be threatened by the heterogeneity of technical approaches, which after all may require a technically specialized audience.
Critical technical practices are generally thought of as a way of reforming the practice of computer science. A crucial question practitioners of critical technical practices will therefore have to answer is how they understand their relationship to those /p.57-58/ outside of computer science pursuing similar projects. In particular, new media art practice is often also a critical technical practice, when artists build complex computational systems (i.e. artworks) which are informed by critical reflection on technology and its role in society. The lines between technical practice, artwork, and cultural studies are blurring, and the space between is becoming home to a new interdiscipline. Hopefully, under this pressure the traditions informing the design and development of computational systems will expand, allowing for an altogether different way of looking at technology in society, and allowing for technical artefacts that enrich human experience, rather than reducing it to a quantified, formalized, efficient, and lifeless existence.

Phoebe Sengers Media Arts Research Studies Institute for Media Communication German National Computer Science Research Center
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