an international and interdisciplinary journal of postmodern cultural sound, text and image

 Volume 11, April - September 2014, ISSN 1552-5112


Apocalypse Not, or How I Learned to Stop Worrying

and Love the Machine


David J. Gunkel

Billy Cripe

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            Often it is the little things that matter, like the difference between two seemingly inconsequential words, the prepositions "through" and "with." But in the area of communication technology everything depends on this distinction. Here's why: When it is employed for the purposes of communication, the computer has customarily been assigned one of two possible positions, both of which are dictated by a particular understanding of the process of communication. The machine has either been defined as a medium through which human users exchange information, or it has occupied, with varying degrees of success, the position of the other in communicative exchange, becoming a participant with whom human users interact. These two alternatives were initially formalized and distinguished by Robert Cathcart and Gary Gumpert in their 1985 essay "The Person-Computer Interaction." In this relatively early text, the authors differentiate interacting through a computer from interacting with a computer. The former, they argue, names all those "computer-facilitated functions" where "the computer is interposed between sender and receiver." The latter designates "person-computer interpersonal functions" where "one party activates a computer which in turn responds appropriately in graphic, alphanumeric, or vocal modes establishing an ongoing sender/receiver relationship" (Cathcart and Gumpert, 1985, p. 114). 

            This difference—the difference between through and with—has important moral consequences. If the computer is situated in the position of a medium through which human users interact and exchange information, then it is operationalized as a more-or-less neutral channel of data transfer and the only subjects in the relationship are the human users who are connected through it. This is a rather intuitive formulation insofar as it recognizes that technology, no matter how sophisticated, is really nothing more than a tool or instrument of human action. If, however, the computer is positioned as the other with whom we interact and exchange data, then things are entirely otherwise. In this circumstance, the computer is not merely a tool of human concourse but is itself another subject, that is, an interactive agent and/or patient in the relationship. Although this sounds, at least initially, to be somewhat counterintuitive, it is supported by recent facts. In fact, the majority of online activity is no longer (and perhaps never really was) human-to-human exchanges but interactions between humans and machines and machines and machines. Current statistics concerning web traffic already give the machines a slight edge with 51% of all traffic being otherwise than human (Foremski, 2012), and this statistic is expected to increase at an accelerated rate (Cisco Systems, 2012).

            The following will trace the challenges and opportunities of this subtle but substantive transformation in contemporary culture—this shift from the machine understood as an instrument through which human users act to the machine as another subject with whom one interacts. Toward this end, we will first examine the advantages and disadvantages of the instrumental view of technology—the standard theory that explains and justifies situating the machine in the intermediate position of through. Second, we will consider recent advancements in posthumanism and autonomous technology that challenge this tradition and provide good reasons to locate the machine in the position of an-other with whom we interact. Third, we will examine the challenges and opportunities of this transformation whereby what had been a mere technological object is recognized as a socially active subject who matters. This is, again, a matter of two small words, and as Jacques Derrida (2005, p. 80) points out, everything turns on and is decided by the difference that separates the "who" from the "what." Finally, we will conclude with an exploration of two ways in which the conversation about machine moral standing might proceed. 



                        1. Default Setting


            Machines—like computers, smart phones, and even sophisticated robots—are technologies, and technologies are mere tools created and used by human beings. The computer means nothing by itself, it is the way it is used that ultimately matters. This common sense assumption is structured and informed by the answer that is typically provided for the question concerning technology.


We ask the question concerning technology when we ask what it is. Everyone knows the two statements that answer our question. One says: Technology is a means to an end. The other says: Technology is a human activity. The two definitions of technology belong together. For to posit ends and procure and utilize the means to them is a human activity. The manufacture and utilization of equipment, tools, and machines, the manufactured and used things themselves, and the needs and ends that they serve, all belong to what technology is (Heidegger 1977, pp. 4-5).


According to Martin Heidegger's analysis, the presumed role and function of any kind of technology, whether it be the product of handicraft or industrialized manufacture, is that it is a means employed by human users for specific ends. Heidegger terms this particular characterization of technology "the instrumental definition" and indicates that it forms what is considered to be the "correct" understanding of any kind of technological device (p. 5).

            As Andrew Feenberg (1991) summarizes it in the introduction to his book Critical Theory of Technology, "The instrumentalist theory offers the most widely accepted view of technology. It is based on the common sense idea that technologies are 'tools' standing ready to serve the purposes of users" (p. 5). And because an instrument "is deemed 'neutral,' without valuative content of its own" (p. 5) a technological artifact is evaluated not in and of itself, but on the basis of the particular employments that have been decided by its human designer or user. Understood as a tool or instrument of human activity, sophisticated technical devices like robots, AIs, algorithms, and other computer systems are not considered the responsible agent of actions that are performed by or through them. "Morality," as AI scientist J. Storrs Hall (2001) points out, "rests on human shoulders, and if machines changed the ease with which things were done, they did not change responsibility for doing them. People have always been the only 'moral agents'" (p. 2).

            Consequently, blaming the computer (or any other technology) is to make at least two mistakes. First, it wrongly attributes agency to something that is a mere instrument or inanimate object. This categorical error mistakenly turns a passive object into an active subject. Second, it allows human users to deflect moral responsibility by putting the blame on something else. In other words, it allows users to "scapegoat the computer," and effectively avoid taking responsibility for their own actions. As Deborah Johnson (2006) succinctly summarizes it: "Computer systems are produced, distributed, and used by people engaged in social practices and meaningful pursuits. This is as true of current computer systems as it will be of future computer systems. No matter how independently, automatic, and interactive computer systems of the future behave, they will be the products (direct or indirect) of human behavior, human social institutions, and human decision" (p. 197).

            The instrumental theory has served us well, and it has helped make sense of all kinds of technological innovation. But all that is over. In other words, the instrumental theory, although a useful instrument for understanding technology, no longer functions as initially designed. It is beginning to show signs of stress, weakness, and even breakdown as the boundary between technology-as-instrument and technology-as-agent become increasingly indistinguishable.




2. The New Normal


            There are, for our purposes, at least two challenges to the instrumental theory of technology. The first, which proceeds from recent work in posthumanism, demonstrates that the one-time clear and distinct line dividing the human from its constitutive others, namely the animal and machine, has become increasingly difficult to define and defend. These innovations do not so much demonstrate that machines are legitimate subjects, but rather critically questions the traditional assumptions surrounding human exceptionalism and technological instrumentalism. The second involves what Langdon Winner calls “autonomous technology,” that is, technologies of various kinds and configurations that are deliberately designed to be more than tools and as a result come to occupy a position that is otherwise than a mere instrument of human action.   


2.1 Posthumanism and Cybernetics

            One challenge to the instrumental tradition comes from recent work in the theory of posthumanism and research in cybernetics. As Donna Haraway argued in the “Cyborg Manifesto,” the line that had once divided the human from the animal and the animal from the machine has become increasingly indistinguishable and leaky:


By the late twentieth century in United States’ scientific culture, the boundary between human and animal is thoroughly breached. The last beachheads of uniqueness have been polluted if not turned into amusement parks—language, tool use, social behavior, mental events, nothing really convincingly settles the separation of the human and animal...[Likewise] late twentieth-century machines have made thoroughly ambiguous the difference between natural and artificial, mind and body, self-developing and externally designed, and many other distinctions that used to apply to organisms and machines. Our machines are disturbingly lively, and we ourselves frighteningly inert (Haraway 1991, 152).


Nowhere is this dual erosion of the boundaries of human exceptionalism more evident than in the Human Genome Project (HGP), a multinational effort to decode and map the totality of genetic information comprising the human species. This project takes deoxyribonucleic acid (DNA) as its primary object of investigation. DNA, on the one hand, is considered to be the fundamental and universal element determining all organic entities, human or otherwise. Understood in this fashion, the difference between the human being and any other life-form is merely a matter of the number and sequence of DNA strings. On the other hand, HGP, following a paradigm that has been central to modern biology, considers DNA to be nothing more than a string of information, a biologically encoded program that is to be decoded, manipulated, and run on a specific information-processing device. This procedure allows for animal bodies to be theorized, understood, and manipulated as mechanisms of information. For this reason, Haraway (1991) concludes that "biological organisms have become biotic systems, communications devices like others.  There is no fundamental, ontological separation in our formal knowledge of machine and organism, of technical and organic" (177-178).

            Haraway uses the term “cyborg,” which she appropriates from a 1962 article on manned space flight (Clynes and Kline 1962), to name this new form of hybridity that is simultaneously both more and less than what has been traditionally considered to be human. According to Haraway, and other posthumanist thinkers who follow her lead (Hayles 2001, Wolfe 2010), the cyborg constitutes a critical intervention in programs of human exceptionalism, making available new configurations of agency, responsibility, and justice. As a result of these critical efforts, technologies are no longer conceptualized as mere tools used by fully-formed, pre-existing, and independent human agents but are already constitutive of the hybrid forms of agency that comprise posthuman subjectivity.

            This blurring of boundaries, however, is not merely theoretical speculation. It has taken empirical form in work surrounding what are now called “biological robots.” And following the work of cyberneticist Kevin Warwick (2010), it is now possible to distinguish three varieties. The first is a mechanical body that is controlled by a biological neuronal network or “brain.” In these cases neurons are cultivated on an electrode array in a closed-loop environment. The neurons grow normally and integrate with the electrode array which is connected (directly or remotely) to the robotic body. As the body encounters obstacles through sensors (sensory input), that information is translated into electrical signals, sent through the electrode array and delivered to the “brain.” The brain responds by generating its own electrical signals, transmitted through the electrode array, and also to the robot body, instructing movement away from the obstacle. Most interestingly, these biological robots demonstrate observable individuality and learn the obstacle avoidance behavior from trial and error while becoming increasingly proficient at their tasks. Neuronal clusters associated with movement and obstacle avoidance grow and are strengthened through repetitive learning, just like human brains (Warwick, 2010). In this context, Warwick’s definition of “brain” is useful to consider: “The term ‘brain’ is employed here to describe the centre of the nervous system – which is responsible for the generation of behaviors and for extracting information from the environment.” (Warwick 2010, ). This is a machine-friendly and practical definition of “brain” that has bearing on this discussion. In this way, “having a brain” is no longer the unique realm of the human or animal. It also belongs to the mechanistic and robotic. And this not through some clever linguistic turn or theoretical speculation, but rather through the practical efforts of real life innovation and scientific experimentation.

            The second kind of biological robot is a biological body that is controlled, at least partially, by a computer–brain interface. This type is even more common than robots controlled by neuronal networks. For instance, St. Jude Medical Device Company recently received approval for their LibraXP deep brain stimulation system. This is a device implanted in the patient’s brain to modulate brain activity and physical manifestations associated with a genetic disease called dystonia (Reuters, 2013).  More extreme examples include what are called “line-following terrestrial insect biobots” (Latif, 2012). In these cases, a computer system delivers electrical impulses directly into the brain of insects to compel them to follow a curving line on the floor, or to fly in certain patterns (in the case of hawkmoth studies). As a result, the actions of the organism are not entirely under its control but are also determined by programmed input.

            The third kind of biological robot is a brain emulation. While long the topic of science fiction, work is underway through The Blue Brain Project at Ecole Polytechnique Federale De Lausanne (EPFL University) in Switzerland. The goal of the Blue Brain Project to emulate a human brain in a computer system by starting with individual neurons and crafting an entire brain from the bottom up (Lehrer, 2008). While it sounds fantastic, the team has already had success emulating cortical clusters. Although findings from this and similar projects are currently being used to develop better treatments for brain afflictions as well as neurological prosthesis, these developments portend outcomes that exceed mere instrumental therapies. If (and when) the EPFL team is able to complete their project, the result will not be just a better prosthesis for the human brain but an artifact that emulates the human sensory and cognitive organ. Or to put it in distinctly Cartesian language, a mind without a body. Similarly DARPA has partnered with the University of California Berkeley to craft synthetic brains based on nanoscale synthetic synapses. These systems are not pre-programmed like a computer. Rather they “learn” and are “self-directed.” At least, that is the plan for the project as it is still underway (Erwin, 2013). These examples of actual machine-animal and machine-human boundary breakdowns demonstrate in very practical terms, the posthuman theories introduced by Haraway and others.



2.2 Autonomous Technology

            Machines are not tools. Although "experts in mechanics," as Karl Marx (1977) pointed out, often confuse the two concepts calling "tools simple machines and machines complex tools" (p. 493), there is an important and crucial difference between the two. "The machine," Marx explains, "is a mechanism that, after being set in motion, performs with its tools the same operations as the worker formerly did with similar tools" (p. 495). Understood in this fashion, the machine occupies the position not of the tool, but of the human worker—the active agent who had used the tool. Evidence of this is already available in the Luddite rebellions, which took place in England between 1811 and 1817. In this case, the newly introduced automatic weaving machines were perceived by English textile artisans not as instruments to be used by human agents in their work but as a direct threat to and replacement for the human worker.

This concept of machinic agency is taken up and further developed by Langdon Winner in his book Autonomous Technology:


To be autonomous is to be self-governing, independent, not ruled by an external law of force. In the metaphysics of Immanuel Kant, autonomy refers to the fundamental condition of free will—the capacity of the will to follow moral laws which it gives to itself. Kant opposes this idea to "heteronomy," the rule of the will by external laws, namely the deterministic laws of nature. In this light the very mention of autonomous technology raises an unsettling irony, for the expected relationship of subject and object is exactly reversed. We are now reading all of the propositions backwards. To say that technology is autonomous is to say that it is nonheteronomous, not governed by an external law. And what is the external law that is appropriate to technology? Human will, it would seem" (Winner 1977, p. 16).


"Autonomous technology," therefore, directly contravenes the instrumental theory by deliberately contesting and relocating the assignment of agency. Such mechanisms are not mere tools employed by human users but occupy, in one way or another, the place of the agent—the other person in social situations and interpersonal interactions. To put it in Kantian language, tools are heterogeneous instruments that are designed, directed, and determined by human will. Machines, however, exceed this conceptualization insofar as they show signs of increasing levels of (self) direction and determination that exceed the reach of human volition and control. 

            Predictions of fully autonomous machines on par with human capabilities is not only the subject of science fiction but is becoming science fact. It can be seen, for instance, in the work of the futurist Ray Kurzweil (2005), AI researcher Hans Moravec (1988), and robotics engineer Rodney Brooks. "Our fantasy machines," Brooks (2002) writes referencing the popular robots of science fiction, "have syntax and technology. They also have emotions, desires, fears, loves, and pride. Our real machines do not. Or so it seems at the dawn of the third millennium. But how will it look a hundred years from now? My thesis is that in just twenty years the boundary between fantasy and reality will be rent asunder" (p. 5). And it may not even take that long as working examples of autonomous technology are already available and in operation in many parts of contemporary culture.

            First, consider what has happened to the financial and commodity markets in the last fifteen years. At one time, trades on the New York Stock Exchange or the Chicago Board Options Exchange were initiated and controlled by human traders. Beginning in the late 1990's, financial services organizations began developing algorithms to take over much of this effort. These algorithms were faster, more efficient, more consistent, and could, as a result of all this, turn incredible profits by exploiting momentary differences in market prices. These algorithms made decisions and initiated actions faster than human comprehension and were designed with learning subroutines in order to respond to new and unanticipated opportunities. And these things worked. They pumped out incredible profits for the financial services industry. As a result, over 70% of all trades are now machine generated and controlled (Slavin 2009).

            What this means is that our finances—not only our mortgages and retirement savings but also a significant part of our nation's economy—is now directed and managed by machines. The consequences of this can be seen in an event called the Flash Crash. At about 2:45 on the 6th of May 2010, the Dow Jones Industrial Average lost over 1000 points in a matter of seconds and then rebounded just a quickly. The drop, which amounted to about 9% of the market's value or 1 trillion dollars, was caused by some “bad” decision making by a couple of trading algorithms. In other words, no human being was in control of the event or could be considered responsible for its occurrence. It was something initiated by the algorithms, and the human brokers could only passively watch events unfold on their monitor screens not knowing what had happened or why. To this day, no one is quite sure what occurred. No one, in other words, knows who or what to blame.

            Similar things are happening in customer service interactions. When you call your bank and apply for credit over the telephone, for instance, your call is often taken by a human operator. This human being, however, is not the active agent in the conversation. He or she is only an interface to a machine, which ultimately decides the outcome of your application. This situation, in fact, inverts the usual roles and assumptions. In the case of credit application decisions, the machine is the active agent and interlocutor. The human operator is only an instrument or interface through which machine generated decisions pass and are conveyed. Although autonomy in this instance is limited to  decisions concerning a very restricted domain (to extend or to deny credit), what is not disputed is the fact that the machine and human being have effectively switched places—the machine occupies the location of the active agent, while the human operator is merely an instrument in service to these machinic decisions.

            A third and final example is drawn from the area of culture—literature, art, music, etc. Currently recommendation algorithms at Netflix and Amazon decide what cultural objects we experience. It is now estimated that 75% of all content that is obtained through Netflix is the result of a machine recommendation (Amatriain & Basilico, 2012). These algorithms are effectively taking over the role of film, book and music critics, influencing—to a significant degree—what films we see, what books we read, and what music we hear. It is important to recognize that far from simply matching similar keywords from one product to another in a pre-defined catalog and calling that the “recommendation,” recommendation systems are self-learning mechanisms. They are designed to identify patterns and connections in an ever growing corpus of data. The term “Big Data” has grown in popularity recently. The data is “big” not because there is a large fixed set of it located in some database on a particular network connected server. Rather it is “big” because it is growing in organic fashion and at exponential rates. Data analytics that deals in the big data field (including decision engines and “next best action” systems) are programmed to be deliberately fuzzy. They allow for and expect new and novel data to be added to their corpus from which they can draw, analyze, and apply algorithms. In this way, the algorithms define the boundary conditions of the system and the corpus provides freedom of self-determination. For such systems, there is no right or wrong answer, only a more or less effective recommendation. Semantic web systems and semantic data processing is one kind of technique currently used in recommendations systems that have large volumes of unstructured data (i.e. data that does not fit well into a row or cell of a database). These systems are inferential and take advantage of patterns that only emerge at large scales.  This means that they look for patterns and conceptual similarity rather than binary matches or deterministic lexical stemming, which are the techniques typically employed in database searches.

            Such conceptual inference, for example, allows these systems to locate, recommend and identify business “colleagues” rather than requiring persons to search for and correlate employee records from many independent systems. This is because they “understand” that colleagues are employees who worked for the same organization during the same time period. Without having a specific metadata attribute of “colleague”, semantic systems are able to infer “colleague,” if they have met the boundary conditions.  Furthermore, the concept of a colleague is not fixed.  It may change over time as new employees are added to an organization and current ones leave. This is just one example of how autonomous or semi-autonomous machine learning and self-deterministic, inferential processing happens today. Newer computer systems use data extraction, snippets, clustering, tuning, ontology-assisted matching, heuristics-based learning and corpus-driven extraction techniques. The items extracted are raw data. But when that data is linked, patterns, clusters and classifications emerge. The interesting part is that it is based on what is inside the content item - the information in the container, rather than what people say about what is inside the container. The ability of machine networks to get into not just the data, but also the meaning of the data, as contextually understood, is a very real and growing phenomenon. This is also why it is so important to ensure that talk of “the machine” is inclusive of the machine network rather than any single node, atomic unit or subset. To do so not only misses out on the opportunity of our new interactions but it also risks conflating the part with the whole.

            But machines are not just involved in recommending cultural products, they are also actively engaged on the evaluative and creative side. In the field of education, machines now qualitatively evaluate and grade student essays. EdX is a nonprofit organization led by Harvard University and MIT that is releasing intelligent algorithm based programs for qualitatively grading student essays (Markoff 2013). This machine system is designed with learning algorithms that are able to understand a wide, varied and ever growing corpus of student work. It is then able to evaluate the content based on the scoring paradigm or rubric it learned from the implementing teacher. Results have been positive (perhaps not so surprisingly), as the machine is able to perform in ways virtually indistinguishable from human beings. This led researchers to conclude, “as a general scoring approach, automated essay scoring appears to have developed to the point where it can be reliably applied in both low-stakes assessment (e.g., instructional evaluation of essays) and perhaps as a second scorer for high-stakes testing” (Shermis 2012, 27). This demonstrates not only the instrumental value of the machine for grading student essays—another useful tool in the arsenal of overworked instructors—but the fact that machines are now taking over what had been perceived to be an exclusively human activity, the reading, evaluating, and grading of written work.

            In the field of journalism, algorithms do not just evaluate compositions but actually perform the writing. Beyond the simple news aggregators that currently populate the web, these programs, like Northwestern University's Stats Monkey, automatically compose publishable stories from machine readable statistical data. Organizations like the Big Ten Network use these systems to generate unique content for web distribution. These programs, although clearly in the early stages of development, recently led Kurt Cagle (managing editor of to provocatively ask whether an AI might compete for and win a Pulitzer Prize by 2030 (Kerwin 2009, p. 1). Similar things are happening in music. The robot metal band Compressorhead, for example, is able to execute Motorhead's "Ace of Spades" and the Ramones' "Blitzkrieg Bop" with a skill and dexterity that often exceeds human capabilities. In the area of classical music, there is EMI or Electronic Musical Interface, an algorithmic composer capable of creating new scores that are virtually indistinguishable from Bach, Chopin, and Beethoven. And then there is Shimon, a marimba playing jazz-bot from Georgia Tech that not only improvises with human musicians in real time, but is capable of creating and performing entirely new musical compositions.


3. The Rise of the Machines

            In November of 2012, General Electric launched a television advertisement called "Robots on the Move." The 60 second video, created by Jonathan Dayton and Valerie Faris (the husband/wife team behind the 2006 feature film Little Miss Sunshine), depicts many of the iconic robots of science fiction traveling across great distances to assemble before some brightly lit airplane hanger for what we are told is the unveiling of some new kind of machines—"brilliant machines," as GE's tagline describes it. And as we observe Robby the Robot from Forbidden Planet, KITT the robotic automobile from Knight Rider, and Lt. Commander Data of Star Trek: The Next Generation making their way to this meeting of artificial minds, we are told, by an ominous voice over, that "the machines are on the move." Although this might not look like your typical robot apocalypse (vividly illustrated in science fiction films and television programs like Terminator, The Matrix Trilogy, and Battlestar Galactica), we are, in fact, in the midst of an invasion. The machines are on the move. They are everywhere and doing everything. They may have begun by displacing workers on the factory floor, but they now actively participate with us in all aspects of our intellectual, social, and cultural existence. This invasion is not some future possibility coming from an alien world. It is here. It is now. And resistance is futile.

            As these increasingly autonomous machines come to occupy influential positions in contemporary culture—positions where they are not just tools or instruments of human action but actors in their own right—we will need to ask ourselves important but difficult questions: At what point might a robot, an algorithm, or other autonomous system be held responsible for the decisions it makes or the actions it deploys? When, in other words, would it make sense to say "It's the computer's fault"? Likewise, at what point might we have to consider seriously extending rights—civil, moral, and legal standing—to these socially aware and interactive devices? When, in other words, would it no longer be considered non-sense to suggest something like "the rights of machines"? In response to these questions, there appears to be at least two options, neither of which are entirely comfortable or satisfactory.

            On the one hand, we can respond as we always have, treating these machines as mere instruments or tools. Joanna Bryson (2010) makes a case for this approach in her provocatively titled essay "Robots Should be Slaves." "My thesis," Bryson writes, "is that robots should be built, marketed and considered legally as slaves, not companion peers" (p. 63). Although this might sound harsh, her argument is persuasive precisely because it draws on and is underwritten by the instrumental theory of technology—a theory that has considerable history and success behind it and that functions as the assumed default position for any consideration of technology. This decision—and it is a decision—has both advantages and disadvantages. On the positive side, it reaffirms human exceptionalism, making it absolutely clear that it is only human beings who have rights and social responsibilities. Technologies, no matter how sophisticated, intelligent, and influential, are and will continue to be mere tools of human action, nothing more. But this approach, for all its usefulness, has a not-so-pleasant downside—it willfully and deliberately produces a new class of slaves and rationalizes this decision as morally justified. This decision also ignores, or at least delays consideration of what appears to be an inevitable evolution and emergence along our current techno-innovation trajectory; the dissolution of the boundary between human and machine.  As we have already demonstrated, instrumentalist advances in machine controlled prosthetics for humans and human-patterned innovations in machine processing force a difficult but necessary reconsidering of what it means to be human-as-such.

            On the other hand, we can decide to entertain a kind of rights of machines just as we had previously done for other non-human entities, like animals and the environment. And there is both moral and legal precedent for this decision. In fact, we already live in a world populated by non-human entities that are considered moral persons. Recently the Indian government recognized dolphins as “non-human persons” (Coelho, 2013) and there is an on-going debate concerning the status of the corporation, which in both US and International law is considered an artificial person, at least for the purposes of contracts, free expression, and other legal adjudications. Once again, this decision sounds reasonable and justified. It extends moral standing to these other socially aware entities and recognizes, following the predictions of Norbert Wiener (1988, p. 16), that the social relationships of the future will involve both humans and machines. But this decision also has a significant cost. It requires that we rethink everything we thought we knew about ourselves, technology, and ethics. It requires that we learn to think beyond human exceptionalism, technological instrumentalism, and all the other -isms that have helped us make sense of our world. No matter how we decide to respond to this machine question, it will have profound effects on how we conceptualize our place in the world, who we decide to include in the community of moral subjects, and what we exclude from such consideration and why.



4. Answering the Machine Question


            Ending with a question, although standard practice in philosophical discourse, is often unsatisfying and can, from a compositional perspective, be considered bad form. As Neil Postman (1993, 181) once described , “anyone who practices the art of cultural criticism must endure being asked, What is the solution to the problems you describe?” Consequently, we conclude by looking at two recent proposals by which to begin formulating a response to this machine question. These solutions are neither complete nor even necessarily consistent. Rather they are offered as a way of contributing positively to the ongoing consideration and debate regarding social responsibility in the 21st century. Despite their differences, both proposals take what is arguably an existentialist approach. Following Jean-Paul Sartre, who famously asserted “existence precedes essence,” we might say that the existence of an entity—human, animal, machine, or otherwise—precedes determinations of its essence. In other words, the fact that it is trumps what it is.   


4.1 Machine Ethics

            The first concerns what is now called Machine Ethics. This relatively new idea was first introduced and publicized in a 2004 Association for the Advancement of Artificial Intelligence paper written by Michael Anderson, Susan Leigh Anderson, and Chris Armen and has been followed by a number of dedicated symposia (Anderson et al, 2005) and publications (Anderson and Anderson 2006 and 2011). Unlike computer ethics, which is mainly concerned with the consequences of human behavior through the instrumentality of technology (Johnson 1993), "machine ethics is concerned," as characterized by Anderson et al. (2004, 1), "with the consequences of behavior of machines toward human users and other machines." In this way, machine ethics both challenges the "human-centric" tradition that has persisted in moral philosophy and argues for a widening of the subject of ethics so as to take into account not only human action with machines, but the behavior of actual machines, namely those that are designed to provide advice or programmed to make autonomous decisions with little or no human supervision.

            Because of this, machine ethics takes an entirely functionalist approach to things. That is, it considers the effect of machine actions on human subjects irrespective of metaphysical debates concerning agency or epistemological problems concerning subjective mind states. As Susan Leigh Anderson (2008, 477) points out, the Machine Ethics project is unique insofar as it, "unlike creating an autonomous ethical machine, will not require that we make a judgment about the ethical status of the machine itself, a judgment that will be particularly difficult to make." Machine Ethics, therefore, does not necessarily deny or affirm the possibility of, for instance, machine consciousness, sentience, or personhood. It simply endeavors to institute a pragmatic approach that does not require that one first decide these questions a priori. It therefore leaves this as an open question and proceeds to ask whether moral decision making is computable and whether machines can in fact be programmed with appropriate ethical standards for social behavior.

            This is a promising innovation insofar as it recognizes that machines are already making decisions and taking real-world actions in such a way that has an effect—an effect that can be evaluated as either good or bad—on human beings and human social institutions. Despite this, the functionalist approach utilized by Machine Ethics has at least three critical difficulties. First, functionalism shifts attention from the cause of an  action to its effects. "Clearly," Anderson and company write (2004, 4), "relying on machine intelligence to effect change in the world without some restraint can be dangerous. Until fairly recently, the ethical impact of a machine's actions has either been negligible, as in the case of a calculator, or, when considerable, has only been taken under the supervision of a human operator, as in the case of automobile assembly via robotic mechanisms. As we increasingly rely upon machine intelligence with reduced human supervision, we will need to be able to count on a certain level of ethical behavior from them." The functionalist approach of Machine Ethics, therefore, derives from and is ultimately motivated by an interest to protect human beings from potentially hazardous machine decision-making and action. This effort is thoroughly and unapologetically anthropocentric. Although effectively opening up the community of moral subjects to other, previously excluded things, the functionalist approach only does so in an effort to protect human interests and investments. This means that the project of Machine Ethics does not differ significantly from computer ethics and its predominantly instrumental and anthropocentric orientation. If computer ethics, as Anderson, Anderson, and Armen (2004) characterize it, is about the responsible and irresponsible use of computerized tools by human users, then their functionalist approach is little more than the responsible programming of machines by human beings for the sake of protecting other human beings.         

            Second, functionalism institutes, as the conceptual flipside and consequence of this anthropocentric privilege, what is arguably a slave ethic. "I follow," Kari Gwen Coleman (2001, 249) writes, "the traditional assumption in computer ethics that computers are merely tools, and intentionally and explicitly assume that the end of computational agents is to serve humans in the pursuit and achievement of their (i.e. human) ends. In contrast to James Gips' call for an ethic of equals, then, the virtue theory that I suggest here is very consciously a slave ethic." For Coleman, computers and other forms of computational agents should, in the words of Bryson (2010), "be slaves." Others, however, are not so confident about the prospects and consequences of this "Slavery 2.0." And this concern is clearly one of the standard plot devices in robot science fiction from R.U.R. and Metropolis to Bladerunner and Battlestar Galactica. But it has also been expressed by contemporary researchers and engineers. Rodney Brooks, for example, recognizes that there are machines that are and will continue to be used and deployed by human users as instruments, tools, and even servants. But he also recognizes that this approach will not cover all machines.


Fortunately we are not doomed to create a race of machine slaves that is unethical to have as human slaves. Our refrigerators work twenty-four hours a day seven days a week, and we do not feel the slightest moral concern for them. We will make many robots that are equally unemotional, unconscious, and unempathetic. We will use them as slaves just as we use our dishwashers, vacuum cleaners, and automobiles today. But those that we make more intelligent, that we give emotions to, and that we empathize with, will be a problem. We had better be careful just what we build, because we might end up liking them, and then we will be morally responsible for their well-being. Sort of like children (Brooks 2002, 195).


According to this analysis, a slave ethic will work, and will do so without any significant moral difficulties or ethical friction, as long as we decide to produce dumb instruments that serve human users as mere instruments and extensions of our will. But as soon as the machines show signs, however minimal defined or rudimentary, that we take to be intelligent, conscious, or intentional, then everything changes. At that point, a slave ethic will no longer be functional or justifiable; it will become morally suspect.

            Finally, machines that are designed to follow rules and operate within the boundaries of some kind of programmed restraint, might turn out to be something other than what is typically recognized as a moral agent. Terry Winograd (1990, 182-183), for example, warns against something he calls "the bureaucracy of mind," -  "where rules can be followed without interpretive judgments." Providing robots, computers, and other autonomous machines with functional morality may produce little more than artificial bureaucrats—decision making mechanisms that can follow rules and protocols but have no sense of what they do or understanding of how their decisions might affect others. "When a person," Winograd (1990, 183) argues, "views his or her job as the correct application of a set of rules (whether human-invoked or computer-based), there is a loss of personal responsibility or commitment. The 'I just follow the rules' of the bureaucratic clerk has its direct analog in 'That's what the knowledge base says.' The individual is not committed to appropriate results, but to faithful application of procedures.

            Mark Coeckelbergh (2010, 236) paints an even more disturbing picture. For him, the problem is not the advent of "artificial bureaucrats," but "psychopathic robots." The term "psychopathy" has traditionally been used to name a kind of personality disorder characterized by an abnormal lack of empathy which is masked by an ability to appear normal in most social situations. Functional morality, Coeckelbergh argues, intentionally designs and produces what are arguably "artificial psychopaths"—robots that have no capacity for empathy but which follow rules and in doing so can appear to behave in morally appropriate ways. These psychopathic machines would, Coeckelbergh (2010, 236) argues, "follow rules but act without fear, compassion, care, and love. This lack of emotion would render them non-moral agents—i.e. agents that follow rules without being moved by moral concerns—and they would even lack the capacity to discern what is of value. They would be morally blind."


4.2 Social Relationalism

            An alternative to moral functionalism can be found in Coeckelbergh's own work on the subject of moral subjectivity. Moral standing, as we have seen, has been typically decided on the basis of essential properties. This “properties approach” is rather straight forward and intuitive: "identify one or more morally relevant properties and then find out if the entity in question has them" (Coeckelbergh 2012, 14). But as Coeckelbergh insightfully points out, there are at least two persistent problems with this undertaking. First, how does one ascertain which properties are sufficient for moral status? In other words, which one, or ones, count? The history of moral philosophy can, in fact, be read as something of an on-going debate and struggle over this matter with different properties—rationality, speech, consciousness, sentience, suffering, etc.—vying for attention at different times. Second, once the morally significant property has been identified, how can one be certain that a particular entity possesses it, and actually possesses it instead of merely simulating it? This is tricky business, especially because most of the properties that are considered morally relevant are internal mental states that are not immediately accessible or directly observable from the outside. In other words, even if it were possible to decide, once and for all, on the right property or mix of properties for moral standing, we would still be confronted and need to contend with a variant of what philosophers call the “other minds problem.”

            In response to these difficulties, Coeckelbergh advances an alternative approach to moral status ascription, which he characterizes as “relational.” By this, he means to emphasize the way moral status is not something located in the inner recesses or essential make-up of an individual entity but transpires through the actually existing interactions and relationships situated between entities. This "relational turn," which Coeckelbergh skillfully develops by capitalizing on innovations in ecophilosophy, Marxism, and the work of Bruno Latour, Tim Ingold, and others, does not get bogged down trying to resolve the philosophical problems associated with the standard properties approach. Instead it recognizes the way that moral status is socially constructed and operationalized. But Coeckelbergh is not content simply to turn things around. Like Friedrich Nietzsche, he knows that simple inversions (in this case, emphasizing the relation instead of the relata) changes little or nothing. So he takes things one step further. Quoting the environmental ethicist Baird Callicot (1995), Coeckelbergh insists that the "relations are prior to the things related" (p. 110). This almost Levinasian gesture is crucial insofar as it undermines the usual way of thinking. It is an anti-Cartesian and postmodern (in the best sense of the word) “intervention.” In Cartesian modernism the individual subject had to be certain of his (and at this time the subject was always gendered male) own being and his own essential properties prior to engaging with others. Coeckelbergh reverses this standard approach. He argues that it is the social that comes first and that the individual subject (an identity construction that is literally thrown under or behind), only coalesces out of the relationship and the assignments of rights and responsibilities that it makes possible. 

            This relational turn in moral thinking is clearly a game changer. As we interact with machines, whether they be pleasant customer service systems, biological robots, or even full or partial brain emulations, the machine is first and foremost situated in relationship to us; with the information we produce and make available to it; with the inferences it makes for and about us; with the predictions it makes about and learns from us. Morality, conceived of in this fashion, is not determined by a prior ontological discovery concerning the essential capabilities, intrinsic properties, or internal operations of others. It is rather a decision—literally a cut that institutes difference and that makes a difference by dividing between who is considered to be morally significant and what is not. Consequently, "moral consideration is," as Mark Coeckelbergh (2010, 214) describes it, "no longer seen as being 'intrinsic' to the entity: instead it is seen as something that is 'extrinsic': it is attributed to entities within social relations and within a social context." This is the reason why, as Levinas (1969, 304) claims, "morality is first philosophy" ("first" in terms of both sequence and status)  and that moral decision making precedes ontological knowledge. Ethics, conceived of in this way, is about decision and not discovery (Putnam 1964, 691). We, individually and in collaboration with each other (and not just those others who we assume are substantially like ourselves), decide who is and who is not part of the moral community—who, in effect, will have been admitted to and included in this first person plural pronoun. This decision, as Anne Foerst (Benford and Malartre 2007, 163) points out, is never certain; it is has always been and continues to be provisional. In effect, and to paraphrase James Carey, we make the rules for ourselves and those we consider Other and then play by them...or not.

            Should machines like AIs, robots, and other autonomous systems be granted admission to the community of moral subjects? This question cannot be answered definitively and finally with a simple "yes" or "no." The question will need to be asked and responded to repeatedly in specific circumstances and the answers that come to be provided will necessarily be provisory and open to ongoing debate. But the questions need to be asked and explicitly addressed rather than being passed over in silence as if they did not matter. As Norbert Wiener (1954, 16) predicted over a half century ago, "Society can only be understood through a study of the messages and the communication facilities which belong to it; and that in the future development of these messages and communication facilities, messages between man and machines, between machines and man, and between machine and machine, are destined to play an ever increasing part." What matters, then, is how one responds, how the terms and conditions of these relationships are decided, and how responsibility comes to be articulated in the face of all these others.



5. Postscript


          A discussion of the moral status of the machine held entirely by humans suggests at least a kind of insensitivity to the very topics considered. So we wanted to get the opinions of at least one machine on this particular subject. We selected Cleverbot ( which is a natural language learning AI that is available to anyone on the web. So we asked Cleverbot the question. The transcript of the conversation (which took place on 4 August 2013 at approximately 8:40am cst) follows:


            Human User: Are you a moral being?


            Cleverbot: Yes, I am. Are you?


            Human User: I believe I am. Why do you believe you are a moral subject?


            Cleverbot: Because you believe that you are a robot.


            Human User: What makes you think that I am a machine?



            Cleverbot: Because you are only responding, you are not taking any activity.





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 Volume 11, April -September 2014, ISSN 1552-5112




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