In this paper, I raise the question whether an artificial intelligence can act morally. I first sketch and defend a general picture of what is at stake in this question. I then sketch and defend a behavioral test, known as the Moral Turing Test, as a good sufficiency test for an artificial intelligence acting morally. I end by discussing some general anticipated objections.

This paper argues against the moral Turing test as a framework for evaluating the moral performance of autonomous systems. Though the term has been carefully introduced, considered, and cautioned about in previous discussions :251–261, 2000; Allen and Wallach 2009), it has lingered on as a touchstone for developing computational approaches to moral reasoning :98–109, 2015). While these efforts have not led to the detailed development of an MTT, they nonetheless retain the idea to discuss what (...) kinds of action and reasoning should be demanded of autonomous systems. We explore the flawed basis of an MTT in imitation, even one based on scenarios of morally accountable actions. MTT-based evaluations are vulnerable to deception, inadequate reasoning, and inferior moral performance vis a vis a system’s capabilities. We propose verification—which demands the design of transparent, accountable processes of reasoning that reliably prefigure the performance of autonomous systems—serves as a superior framework for both designer and system alike. As autonomous social robots in particular take on an increasing range of critical roles within society, we conclude that verification offers an essential, albeit challenging, moral measure of their design and performance. (shrink)

Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24–28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of (...) class='Hi'>moral evaluations when blinded to their source. Remarkably, they rated the AI’s moral reasoning as superior in quality to humans’ along almost all dimensions, including virtuousness, intelligence, and trustworthiness, consistent with passing what Allen and colleagues call the comparative MTT. Next, when tasked with identifying the source of each evaluation (human or computer), people performed significantly above chance levels. Although the AI did not pass this test, this was not because of its inferior moral reasoning but, potentially, its perceived superiority, among other possible explanations. The emergence of language models capable of producing moral responses perceived as superior in quality to humans’ raises concerns that people may uncritically accept potentially harmful moral guidance from AI. This possibility highlights the need for safeguards around generative language models in matters of morality. (shrink)

I reply to professor Proudfoot’s reply to my paper ‘The Moral Turing Test: a defense’ (2024).

Purpose – The purpose of this paper is to explore artificial moral agency by reflecting upon the possibility of a Moral Turing Test and whether its lack of focus on interiority, i.e. its behaviouristic foundation, counts as an obstacle to establishing such a test to judge the performance of an Artificial Moral Agent. Subsequently, to investigate whether an MTT could serve as a useful framework for the understanding, designing and engineering of AMAs, we set (...) out to address fundamental challenges within the field of robot ethics regarding the formal representation of moral theories and standards. Here, typically three design approaches to AMAs are available: top-down theory-driven models and bottom-up approaches which set out to model moral behaviour by means of models for adaptive learning, such as neural networks, and finally, hybrid models, which involve components from both top-down and bottom-up approaches to the modelling of moral agency. With inspiration from Allen and Wallace as well as Prior, we elaborate on theoretically driven approaches to machine ethics by introducing deontic tense logic. Finally, within this framework, we explore the character of human interaction with a robot which has successfully passed an MTT. Design/methodology/approach – The ideas in this paper reflect preliminary theoretical considerations regarding the possibility of establishing a MTT based on the evaluation of moral behaviour, which focusses on moral reasoning regarding possible actions. The thoughts reflected fall within the field of normative ethics and apply deontic tense logic to discuss the possibilities and limitations of artificial moral agency. Findings – The authors stipulate a formalisation of logic of obligation, time and modality, which may serve as a candidate for implementing a system corresponding to an MTT in a restricted sense. Hence, the authors argue that to establish a present moral obligation, we need to be able to make a description of the actual situation and the relevant general moral rules. Such a description can never be complete, as the combination of exhaustive knowledge about both situations and rules would involve a God eye’s view, enabling one to know all there is to know and take everything relevant into consideration before making a perfect moral decision to act upon. Consequently, due to this frame problem, from an engineering point of view, we can only strive for designing a robot supposed to operate within a restricted domain and within a limited space-time region. Given such a setup, the robot has to be able to perform moral reasoning based on a formal description of the situation and any possible future developments. Although a system of this kind may be useful, it is clearly also limited to a particular context. It seems that it will always be possible to find special cases in which a given system does not pass the MTT. This calls for a new design of moral systems with trust-related components which will make it possible for the system to learn from experience. Originality/value – It is without doubt that in the near future we are going to be faced with advanced social robots with increasing autonomy, and our growing engagement with these robots calls for the exploration of ethical issues and stresses the importance of informing the process of engineering ethical robots. Our contribution can be seen as an early step in this direction. (shrink)

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and (...) neither Bohn’s version of the test nor Allen, Varner and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated. (shrink)

Given actual autonomous systems with capacities for harm and the public’s apparent willingness to take moral advice from large language models (LLMs), Einar Duenger Bohn’s (2024) renewed discussion of the Moral Turing Test (MTT) is timely. Bohn’s aim is to defend an unequivocally behavioural test. In this paper, I argue against this direction. Interpreted as testing mere behaviour, the Turing test is a poor test of either intelligence or moral agency, and (...) neither Bohn’s version of the test nor Allen, Varner and Zinser’s influential (2000) version avoids these problems. Also, the MTT’s advantages as advertised by Bohn do not hold up: it is an open empirical question whether embracing a merely behavioural MTT will significantly reduce the challenges in building a computer to pass the test. This issue aside, I argue that Turing’s actual test is superior to current AI benchmarks and to Bohn’s version of the test. A test of moral reasoning or agency in machines that is based on Turing’s actual test has advantages over current tests of moral reasoning in AI, including versions of the MTT. Such a test is an intriguing possibility still to be investigated. (shrink)

이 논문에서 우리는 인공지능 행위자의 행위의 도덕성 검증을 위한 하나의 기준을 제시하고 그것의 이론적 토대를 설계하고자 한다. 이를 위해 인공적 도덕 행위자의 도덕성 수준을 10세 아동 수준으로 설정하고 그것의 도덕성 검증을 위한 전형으로 앨런 튜링(A. Turing)의 「Computing machinery and intelligence」로부터 전개되는 튜링테스트를 취한다. 행동주의(Behaviorism)에 입각하여 인공지능, 구체적으로 말해 컴퓨터의 사고가능성을 판단하기 위한 사고실험인 튜링테스트의 원칙과 방법에 착안하여 인공지능 행위자의 도덕성소유여부, 나아가 그것의 단계를 구획할 수 있는 윤리학적 기준을 제시해 보겠다. 그 배경으로 콜버그(L. Kohlberg)의 인지발달론에 따른 도덕판단 3수준구분을 취하고, 이로부터 (...) 인공적 도덕행위자의 ‘명령의 무조건적 수행’, ‘상벌에 따른 결과주의’, ‘사회적 규약의 준수’라는 도덕발달 3단계를 도출하겠다. 그리고 이를 가정용 헬스케어 로봇 시나리오 예를 검토함으로써 실생활에의 적용 가능성을 타진하겠다. (shrink)

What would it take for an artificial agent to be treated as having moral value? As a first step toward answering this question, we ask what it would take for an artificial agent to be capable of the sort of autonomous, adaptive social behavior that is characteristic of the animals that humans interact with. We propose that this sort of capacity is best measured by what we call the Embodied Turing Test. The Embodied Turing test (...) is a test in which intelligence is operationally defined in terms of autonomous, adaptive interaction with the environment and with other animals. Three versions of the Embodied Turing test were performed with a SONY AIBO robot. Human participants were asked to differentiate between AIBO in a human-controlled mode and AIBO in a software-controlled mode. Our results indicate that the human participants were guessing at how AIBO was controlled. Our data reveals that people do not have enough experience with robots to accurately evaluate its behavior. This indicates that today’s humans do not have enough experience with artificial agents to treat them as morally valuable. (shrink)

If, as a number of writers have predicted, the computers of the future will possess intelligence and capacities that exceed our own then it seems as though they will be worthy of a moral respect at least equal to, and perhaps greater than, human beings. In this paper I propose a test to determine when we have reached that point. Inspired by Alan Turing’s (1950) original “Turing test”, which argued that we would be justified in (...) conceding that machines could think if they could fill the role of a person in a conversation, I propose a test for when computers have achieved moral standing by asking when a computer might take the place of a human being in a moral dilemma, such as a “triage” situation in which a choice must be made as to which of two human lives to save. We will know that machines have achieved moral standing comparable to a human when the replacement of one of these people with an artificial intelligence leaves the character of the dilemma intact. That is, when we might sometimes judge that it is reasonable to preserve the continuing existence of a machine over the life of a human being. This is the “Turing Triage Test”. I argue that if personhood is understood as a matter of possessing a set of important cognitive capacities then it seems likely that future AIs will be able to pass this test. However this conclusion serves as a reductio of this account of the nature of persons. I set out an alternative account of the nature of persons, which places the concept of a person at the centre of an interdependent network of moral and affective responses, such as remorse, grief and sympathy. I argue that according to this second, superior, account of the nature of persons, machines will be unable to pass the Turing Triage Test until they possess bodies and faces with expressive capacities akin to those of the human form. (shrink)

In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that (...) complexify the application of this test; and, (3) in doing so, defend a way of thinking about the question of the moral standing of intelligent machines, which takes the idea of “seriousness” seriously. This last objective is, in fact, my primary one and is motivated by the sense that, to date, much of the “philosophy” of AI has suffered from a profound failure to properly distinguish between things that we can say and things that we can really mean. (shrink)

For many, the very idea of an artificialintelligence has always been ethicallytroublesome. The putative ability of machinesto mimic human intelligence appears to callinto question the stability of taken forgranted boundaries between subject/object,identity/similarity, free will/determinism,reality/simulation, etc. The artificiallyintelligent object thus appears to threaten thehuman subject with displacement and redundancy.This article takes as its starting point AlanTuring''s famous ''imitation game,'' (the socalled ''Turing Test''), here treated as aparable of the encounter between human originaland machine copy – the born and the (...) made. Thecultural resonances of the recent on-lineperformance of a ''Turing Test'' for computergenerated art are then explored. Arttraditionally taken to stand for all that isconsidered quintessentially human – andtherefore resistant to mechanisation –represents in this sense a kind of ''criticalcase'' in the advance of machine intelligence.The article focuses on the moral status of thebody, human agency, and social knowledge in theongoing (re-)constructions of copy, original,and of the difference between them. (shrink)

Artificial intelligence (AI) has become a transformative force in the legal domain, automating complex tasks such as contract analysis, compliance checks, and legal research. However, the intersection of AI and moral decision-making exposes significant limitations. Legal systems are not merely instruments for enforcing rules—they are platforms where human morality, intent, and societal impact are weighed. This paper explores the critical question: Can AI truly deliver justice, or does it merely replicate historical biases encoded in training data? Using the concept (...) of the “Moral Turing Test,” the paper argues that AI lacks the capacity for ethical reasoning and moral discretion, which are fundamental to adjudicating complex legal disputes. Case studies, including Estonia’s AI judges for small claims and the controversial COMPAS algorithm in the United States, illustrate the risks of bias and accountability gaps in AI-driven legal decision-making. The paper advocates for a Human-in-the-Loop (HITL) framework, where AI assists but does not replace human judgment, ensuring that moral reasoning and accountability remain central to the legal process. It concludes that while AI holds promise in enhancing efficiency, it must be carefully regulated to preserve the ethical foundations of justice. (shrink)

To assess the intelligence of machines, Alan Turing proposed a test of imitation known as the imitation game, famously known as the Turing test. To assess whether artificial intelligent (AI) systems could be moral or not, Colin Allen et al. developed a test of imitation in the context of morality, a test known as the Moral Turing Test (MTT), which I will, in this paper, call the moral imitation game. (...) There are arguments against developing any type of MTT or moral imitation game. Rather than developing a moral imitation game, this paper proposes a criterion of imitation for AI systems in the moral scenario. To develop a criterion of imitation, I explore the notion of moral attribution in detail. Within the case of moral attribution, I introduce the subject‐ascriber distinction. The notion of moral attribution and the subject‐ascriber distinction is employed to address the following questions: (a) how is the assessment in MTT arranged?, (b) what is the role of the interrogator in the MTT?, and (c) what information is to be concealed from the interrogator? The first question deals with the aspect of assessment, the second question deals with the reordering of the role of the interrogator and the third one deals with the concealment aspect of the imitation game. After that, a criterion of imitation for AI systems is put forward. Using the subject‐ascriber distinction in moral attribution and David Lewis' Mad Pain and Martian Pain cases, it is argued that the notion of sentience is insignificant for the ascriber for moral attributions. (shrink)

As arti® cial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an arti® cial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing arti® cial moral agents result both from controversies among ethicists about moral theory (...) itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a `moral Turing Test ’ is considered and the computational di culties accompanying the diŒerent types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people and other sentient beings are great. The development of machines with enough intelligence to assess the eŒects of their actions on sentient beings and act accordingly may ultimately be the most important task faced by the designers of arti® cially intelligent automata. (shrink)

In his famous 1950 paper where he presents what became the benchmark for success in artificial intelligence, Turing notes that "at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted" (Turing 1950, 442). Kurzweil (1990) suggests that Turing's prediction was correct, even if no machine has yet to pass the Turing Test. (...) In the wake of the computer revolution, research in artificial intelligence and cognitive science has pushed in the direction of interpreting "thinking" as some sort of computational process. On this understanding, thinking is something computers (in principle) and humans (in practice) can both do. It is difficult to say precisely when in history the meaning of the term "thinking" headed in this direction. Signs are already present in the mechanistic and mathematical tendencies of the early Modern period, and maybe even glimmers are apparent in the ancient Greeks themselves. But over the long haul, we somehow now consider "thinking" as separate from the categories of "thoughtfulness" (in the general sense of wondering about things), "insight" and "wisdom." Intelligent machines are all around us, and the world is populated with smart cars, smart phones and even smart (robotic) appliances. But, though my cell phone might be smart, I do not take that to mean that it is thoughtful, insightful or wise. So, what has become of these latter categories? They seem to be bygones left behind by scientific and computational conceptions of thinking and knowledge that no longer have much use for them. In 2000, Allen, Varner and Zinser addressed the possibility of a Moral Turing Test (MTT) to judge the success of an automated moral agent (AMA), a theme that is repeated in Wallach and Allen (2009).. (shrink)

In this paper, we take moral agency to be that context in which a particular agent can, appropriately, be held responsible for her actions and their consequences. In order to understand moral agency, we will discuss what it would take for an artifact to be a moral agent. For reasons that will become clear over the course of the paper, we take the artifactual question to be a useful way into discussion but ultimately misleading. We set out (...) a number of conceptual pre-conditions for being a moral agent and then outline how one should — and should not — go about attributing moral agency. In place of a litmus test for such an agency — such as Allen et al.'s Moral Turing Test — we suggest some tools from the conceptual spaces theory and the unified conceptual space theory for mapping out the nature and extent of that agency. (shrink)

A provocative exploration of how computers are reshaping ideas about what it means to be human profiles the annual Turing Test to assess a computer's capacity for thought while analyzing related philosophical, biological and moral issues.

In modern technical societies computers interact with human beings in ways that can affect moral rights and obligations. This has given rise to the question whether computers can act as autonomous moral agents. The answer to this question depends on many explicit and implicit definitions that touch on different philosophical areas such as anthropology and metaphysics. The approach chosen in this paper centres on the concept of information. Information is a multi-facetted notion which is hard to define comprehensively. (...) However, the frequently used definition of information as data endowed with meaning can promote our understanding. It is argued that information in this sense is a necessary condition of cognitivist ethics. This is the basis for analysing computers and information processors regarding their status as possible moral agents. Computers have several characteristics that are desirable for moral agents. However, computers in their current form are unable to capture the meaning of information and therefore fail to reflect morality in anything but a most basic sense of the term. This shortcoming is discussed using the example of the Moral Turing Test. The paper ends with a consideration of which conditions computers would have to fulfil in order to be able to use information in such a way as to render them capable of acting morally and reflecting ethically. (shrink)

Artificial agents such as robots are performing increasingly significant ethical roles in society. As a result, there is a growing literature regarding their moral status with many suggesting it is justified to regard manufactured entities as having intrinsic moral worth. However, the question of whether artificial agents could have the high degree of moral status that is attributed to human persons has largely been neglected. To address this question, the author developed a respect-based account of the ethical (...) criteria for the moral status of persons. From this account, the paper employs an empirical test that must be passed in order for artificial agents to be considered alongside persons as having the corresponding rights and duties. (shrink)

The Turing test has been studied and run as a controlled experiment and found to be underspecified and poorly designed. On the other hand, it has been defended and still attracts interest as a test for true artificial intelligence (AI). Scientists and philosophers regret the test’s current status, acknowledging that the situation is at odds with the intellectual standards of Turing’s works. This article refers to this as the Turing Test Dilemma, following the (...) observation that the test has been under discussion for over seventy years and still is widely seen as either too bad or too good to be a valuable experiment for AI. An argument that solves the dilemma is presented, which relies on reconstructing the Turing test as a thought experiment in the modern scientific tradition. It is argued that Turing’s exposition of the imitation game satisfies Mach’s characterization of the basic method of thought experiments and that Turing’s uses of his test satisfy Popper’s conception of the critical and heuristic uses of thought experiments and Kuhn’s association of thought experiments to conceptual change. It is emphasized how Turing methodically varied the imitation game design to address specific challenges posed to him by other thinkers and how his test illustrates a property of the phenomenon of intelligence and suggests a hypothesis on machine learning. This reconstruction of the Turing test provides a rapprochement to the conflicting views on its value in the literature. (shrink)

Discussions of artificial intelligence have been shaped by two brilliant thought-experiments: Turing’s Imitation Test for thinking systems and Searle’s Chinese Room Argument. In many ways, debates about large language models (LLMs) struggle to move beyond these original, opposing thought-experiments. So, in this paper, I ask whether we can move debate forward by exploring the features Sceptics about LLM abilities take to ground meaning. Section 1 sketches the options, while Sections 2 and 3 explore the common requirement for a (...) robust relation between linguistic signs and external objects. Section 3 argues that concerns about worldly connections can be met and thus that outputs of LLMs should be viewed as genuinely meaningful. Section 4 then turns to the argument that the kind of derived meaning explored in Section 3 is insufficient for LLMs to count as meaningful systems per se, looking at the claim that they must possess original intentionality before we admit them to the space of meaning. I argue that this demand is not a prerequisite for meaning per se, but rather for some kinds of agency or conscious understanding. I suggest that the demands for original intentionality are not currently met by LLMs and that we should be cautious about whether we want them to be. (shrink)

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and similar topics in philosophy (...) of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic. -/- (This paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.). (shrink)

Turing''s test has been much misunderstood. Recently unpublished material by Turing casts fresh light on his thinking and dispels a number of philosophical myths concerning the Turing test. Properly understood, the Turing test withstands objections that are popularly believed to be fatal.

Stuart M. Shieber’s name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM. 1 With this collection, I expect it to become equally well known to philosophers.

This paper provides a survey of philosophical discussion of the "the Turing Test". In particular, it provides a very careful and thorough discussion of the famous 1950 paper that was published in Mind.

The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last (...) fifty years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960's to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now. (shrink)

In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, (...) so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one’s own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a “minimal” Turing test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants’ explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing test, it should be able to generate this sort of reciprocal contingencies. (shrink)

There has been considerable optimistic speculation on how well ChatGPT-4 would perform in a Turing Test. However, no minimally serious implementation of the test has been reported to have been carried out. This brief note documents the re-sults of subjecting ChatGPT-4 to 10 Turing Tests, with different interrogators and participants. The outcome is tremendously disappointing for the optimists. Despite ChatGPT reportedly outperforming 99.9% of humans in a Verbal IQ test, it falls short of passing the (...) Turing Test. In 9 out of the 10 tests conducted, the interroga-tors successfully identified ChatGPT-4 and the human participant. The probability of obtaining this result from a process in which the interrogator is really no better than chance at correct identification is calculated to be less than 1%. An additional question was posed to the interrogators at the end of each test: What led them to distinguish between the human and the machine? The interrogators, who effectively filtered out ChatGPT-4 from passing the Turing Test for intelligence, stated that they could identify the machine because it, in effect, responded more intelligently than the human. Subsequently, ChatGPT-4 was tasked with differentiating syntax from semantics and self-corrected when falling for the fallacy of equivocation. The curious situation is arrived at that passing the Turing Test for intelligence remains a challenge that ChatGPT-4 has yet to overcome, precisely because, as per the inter-rogators, its intellectual abilities surpass those of individual humans. (shrink)

The Turing Test is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these (...) systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds. (shrink)

In 1950, Alan Turing proposed his eponymous test based on indistinguishability of verbal behavior as a replacement for the question "Can machines think?" Since then, two mutually contradictory but well-founded attitudes towards the Turing Test have arisen in the philosophical literature. On the one hand is the attitude that has become philosophical conventional wisdom, viz., that the Turing Test is hopelessly flawed as a sufficient condition for intelligence, while on the other hand is the (...) overwhelming sense that were a machine to pass a real live full-fledged Turing Test, it would be a sign of nothing but our orneriness to deny it the attribution of intelligence. The arguments against the sufficiency of the Turing Test for determining intelligence rely on showing that some extra conditions are logically necessary for intelligence beyond the behavioral properties exhibited by an agent under a Turing Test. Therefore, it cannot follow logically from passing a Turing Test that the agent is intelligent. I argue that these extra conditions can be revealed by the Turing Test, so long as we allow a very slight weakening of the criterion from one of logical proof to one of statistical proof under weak realizability assumptions. The argument depends on the notion of interactive proof developed in theoretical computer science, along with some simple physical facts that constrain the information capacity of agents. Crucially, the weakening is so slight as to make no conceivable difference from a practical standpoint. Thus, the Gordian knot between the two opposing views of the sufficiency of the Turing Test can be cut. (shrink)

The Turing test, which is a verbal test of the indistinguishability of machine and human intelligence, is a historically important idea that has set a way of thinking about the AI (artificial intelligence) project that is still relevant today. According to it, the benchmark/blueprint for AI is human intelligence, and the key skill of AI should be its communicative proficiency – which includes explaining decisions made by the machine. Passing the original Turing test by a (...) machine does not guarantee that the machine is, from the point of view of a human, trustworthy; on the contrary, the idea of a machine capable of “fooling” or “outsmarting” a human is inherent within the concept of the test. We postulate, that a test that guarantees a high level of trust should be: a) non-imitative, b) non-behavioral, c) focused on the ability to explain, taking into account, however, the design and rules of operation of the machine (and not just human expectations), d) focused on the machine’s ability to learn. (shrink)

In this paper we discuss the views on the Turing test of four influential thinkers who belong to the tradition of analytic philosophy: Ludwig Wittgenstein, Noam Chomsky, Hilary Putnam and John Searle. Based on various beliefs about philosophical and/or linguistic matters, they arrive at different assessments of both the significance and suitability of the imitation game for the development of cognitive science and AI models. Nevertheless, they share a rejection of the idea that one can treat Turing (...) test as a test for “machine thinking.” This seems to stem from a concern for the proper use of language —one that is a fundamental methodological commitment of analytic philosophy. (shrink)

I have discussed the frame problem and the Turing test at length, but I have not attempted to spell out what I think the implications of the frame problem ...

This paper discusses some difficulties in understanding the Turing test. It emphasizes the importance of distinguishing between conceptual and empirical perspectives and highlights the former as introducing more serious problems for the TT. Some objections against the Turingian framework stemming from the later Wittgenstein’s philosophy are exposed. The following serious problems are examined: 1) It considers a unique and exclusive criterion for thinking which amounts to their identification; 2) it misidentifies the relationship of speaking to thinking as that (...) of a criterion; 3) it neglects the “natural” course of the development in semantics. However, these considerations suggest only that it is problematic to label a successful chatbot as a “thinking entity” without further qualifications, but not necessarily and once and for all incorrect. Philosophy has only little to say about the technical possibility of creating such an effective program. (shrink)

Brain damage can cause massive changes in consciousness levels. From a clinical and ethical point of view it is desirable to assess the level of residual consciousness in unresponsive patients. However, no direct measure of consciousness exists, so we run into the philosophical problem of other minds. Neurologists often make implicit use of a Turing test-like procedure in an attempt to gain access to damaged minds, by monitoring and interpreting neurobehavioral responses. New brain imaging techniques are now being (...) developed that permit communication with unresponsive patients, using their brain signals as carriers of messages relating to their mental states. (shrink)

I advocate a theory of syntactic semantics as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (...) (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is syntactic understanding. (3) An internal (or narrow ), first-person point of view makes an external (or wide ), third-person point of view otiose for purposes of understanding cognition. (shrink)

The Turing Test is routinely understood as a behaviourist test for machine intelligence. Diane Proudfoot has argued for an alternative interpretation. According to Proudfoot, Turing’s claim that intelligence is what he calls ‘an emotional concept’ indicates that he conceived of intelligence in response-dependence terms. As she puts it: ‘Turing’s criterion for “thinking” is…: x is intelligent if in the actual world, in an unrestricted computer-imitates-human game, x appears intelligent to an average interrogator’. The role of (...) the famous test is thus to provide the conditions in which to examine the average interrogator’s responses. I shall argue that Proudfoot’s analysis falls short. The philosophical literature contains two main models of response-dependence, what I shall call the transparency model and the reference-fixing model. Proudfoot resists the thought that Turing might have endorsed one of these models to the exclusion of the other. But the details of her own analysis indicate that she is, in fact, committed to the claim that Turing’s account of intelligence is grounded in a transparency model, rather than a reference-fixing one. By contrast, I shall argue that while Turing did indeed conceive of intelligence in response-dependence terms, his account is grounded in a reference-fixing model, rather than a transparency one. This is fortunate, because, as an account of intelligence, the transparency model is arguably problematic in a way that the reference-fixing model isn’t. (shrink)

The main factor of intelligence is defined as the ability tocomprehend, formalising this ability with the help of new constructsbased on descriptional complexity. The result is a comprehension test,or C- test, which is exclusively defined in computational terms. Due toits absolute and non-anthropomorphic character, it is equally applicableto both humans and non-humans. Moreover, it correlates with classicalpsychometric tests, thus establishing the first firm connection betweeninformation theoretical notions and traditional IQ tests. The TuringTest is compared with the C- (...) class='Hi'>test and the combination of the two isquestioned. In consequence, the idea of using the Turing Test as apractical test of intelligence should be surpassed, and substituted bycomputational and factorial tests of different cognitive abilities, amuch more useful approach for artificial intelligence progress and formany other intriguing questions that present themselves beyond theTuring Test. (shrink)

The Turing Test is best regarded as a model to test for intelligence, where an entity’s intelligence is inferred from its ability to be attributed with ‘human-likeness’ during a text-based conversation. The problem with this model, however, is that it does not care if or how well an entity produces a meaningful conversation, as long as its interactions are humanlike enough. As a consequence, the TT attracts projects that concentrate on how best to fool the judges. In (...) light of this, I propose a new version of the TT: the Questioning Turing Test. Here, the entity has to produce an enquiry rather than a conversation; and it is parametrised along two further dimensions in addition to ‘human-likeness’: ‘correctness’, evaluating if the entity accomplishes the enquiry; and ‘strategicness’, evaluating how well the entity accomplishes the enquiry, in terms of the number of questions asked. (shrink)

A encyclopedia entry on the moral impacts that happen when information technologies are used to record, communicate and organize information. including the moral challenges of information technology, specific moral and cultural challenges such as online games, virtual worlds, malware, the technology transparency paradox, ethical issues in AI and robotics, and the acceleration of change in technologies. It concludes with a look at information technology as a model for moral change, moral systems and moral agents.

Alan Turing, einer der Gründerväter der modernen Informatik, diskutierte in seinem 1950 veröffentlichten Artikel »Computing Machinery and Intelligence« die Frage, ob Maschinen denken können. Dies wirft jedoch die schwierige Frage auf, was Denken denn sei. Um diese Frage zu umgehen, schlägt Turing vor, stattdessen eine Frage zu stellen, die sich durch bloße Beobachtung klären lässt, nämlich ob eine Maschine ein bestimmtes Spiel erfolgreich spielen könne.

Mahadevan (2018, AAAI Conference. https://people.cs.umass.edu/~mahadeva/papers/aaai2018-imagination.pdf) proposes that we are at the cusp of imagination science, one of whose primary concerns will be the design of imagination machines. Programs have been written that are capable of generating jokes (Kim Binsted’s JAPE), producing line-drawings that have been exhibited at such galleries as the Tate (Harold Cohen’s AARON), composing music in several styles reminiscent of such greats as Vivaldi and Mozart (David Cope’s Emmy), proving geometry theorems (Herb Gelernter’s IBM program), and inducing quantitative (...) laws from empirical data (Pat Langley, Gary Bradshaw, Jan Zytkow, and Herbert Simon’s BACON). In recent years, Dartmouth has been hosting Turing Tests in creativity in three categories: short stories, sonnets, and dance music DJ sets. In this post, I will provide a brief and non-exhaustive survey of some plausible responses to these imagination machines and the related prospects for our understanding of the imagination. (shrink)

Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why (...) did Turing refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing’s 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. (shrink)