While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...) robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question - also explored in the experiment - whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook. (shrink)

The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon between (...) health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility. (shrink)

Could a sufficiently detailed computer simulation of consciousness replicate consciousness? In other words, is performing the right computations sufficient for artificial consciousness? Or will there remain a difference between simulating and being a conscious system, because the right computations must be implemented in the right way? From the perspective of Karl Friston's free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial systems, but are not instantiated by computers with a (...) classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness. Since this property is instantiated by all systems that conform to the free energy principle (not just conscious beings), the account on offer here can be extended to draw a distinction between simulating and being a certain type of system, more generally. In particular, this may inform meta-ethical accounts of artificial moral status and artificial moral agency. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater attention from the private and the public sectors. Estimates indicate that the market for AI in cybersecurity will grow from US$1 billion in 2016 to a US$34.8 billion net worth by 2025. The latest national cybersecurity and defence strategies of several governments explicitly mention AI capabilities. At the same time, initiatives to define new standards and certification procedures to elicit users’ trust in AI are emerging on a global scale. (...) However, trust in AI (both machine learning and neural networks) to deliver cybersecurity tasks is a double edged sword: it can improve substantially cybersecurity practices, but can also facilitate new forms of attacks to the AI applications themselves, which may pose severe security threats. We argue that trust in AI for cybersecurity is unwarranted and that, to reduce security risks, some form of control to ensure the deployment of ‘reliable AI’ for cybersecurity is necessary. To this end, we offer three recommendations focusing on the design, development and deployment of AI for cybersecurity. (shrink)

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of a ‘good (...) AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (shrink)

For an artificial agent to be morally praiseworthy, its rules for behaviour and the mechanisms for supplying those rules must not be supplied entirely by external humans. Such systems are a substantial departure from current technologies and theory, and are a low prospect. With foreseeable technologies, an artificial agent will carry zero responsibility for its behavior and humans will retain full responsibility.

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to the collection of partial data, rare updates, and significant demands on resources. To address these issues, the article suggests that specific data management and Machine Learning (ML) techniques, such as natural language processing (...) and sentiment analysis, can improve the measurement (ML about) and practice (ML for) of IPD. The article concludes by considering some of the principal risks of ML for IPD, including concerns over data privacy, the potential for manipulation, and the dangers of overreliance on technology. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Which artificial intelligence (AI) systems are agents? To answer this question, I propose a multidimensional account of agency. According to this account, a system's agency profile is jointly determined by its level of goal-directedness and autonomy as well as is abilities for directly impacting the surrounding world, long-term planning and acting for reasons. Rooted in extant theories of agency, this account enables fine-grained, nuanced comparative characterizations of artificial agency. I show that this account has multiple important virtues and is more (...) informative than alternatives. More speculatively, it may help to illuminate two important emerging questions in AI ethics: 1. Can agency contribute to the moral status of non-human beings, and how? 2. When and why might AI systems exhibit power-seeking behaviour and does this pose an existential risk to humanity? (shrink)

Why would we want to develop artificial human-like arithmetical intelligence, when computers already outperform humans in arithmetical calculations? Aside from arithmetic consisting of much more than mere calculations, one suggested reason is that AI research can help us explain the development of human arithmetical cognition. Here I argue that this question needs to be studied already in the context of basic, non-symbolic, numerical cognition. Analyzing recent machine learning research on artificial neural networks, I show how AI studies could potentially shed (...) light on the development of human numerical abilities, from the proto-arithmetical abilities of subitizing and estimating to counting procedures. Although the current results are far from conclusive and much more work is needed, I argue that AI research should be included in the interdisciplinary toolbox when we try to explain the development and character of numerical cognition and arithmetical intelligence. This makes it relevant also for the epistemology of mathematics. (shrink)

This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values in to AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used to (...) further strengthen design coordination efforts. VSD is shown to be both able to distill these common values as well as provide a framework for stakeholder coordination. (shrink)

Can artificial entities be worthy of moral considerations? Can they be artificial moral agents (AMAs), capable of telling the difference between good and evil? In this essay, I explore both questions—i.e., whether and to what extent artificial entities can have a moral status (“the machine question”) and moral agency (“the AMA question”)—in light of Kazuo Ishiguro’s 2021 novel Klara and the Sun. I do so by juxtaposing two prominent approaches to machine morality that are central to the novel: the (1) (...) view “from within,” including the standard (or “metaphysical”) perspective on moral agency, and the (2) view “from outside,” which includes behaviorism, functionalism and the social-relational perspective. Importantly, while the story illustrates both views, it exposes the epistemological vulnerability of the first in relation to the practical and social reality imposed by the second. That is, regardless of what metaphysical properties the Artificial Friend Klara can be said to have (from within), her moral status as well as agency ultimately depend on the views of others (from outside), including the others’ own epistemic beliefs about the nature of consciousness and personhood. (shrink)

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse (...) fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. (shrink)

This paper investigates the concept of behavioral autonomy in Artificial Life by drawing a parallel to the use of teleological notions in the study of biological life. Contrary to one of the leading assumptions in Artificial Life research, I argue that there is a significant difference in how autonomous behavior is understood in artificial and biological life forms: the former is underlain by human goals in a way that the latter is not. While behavioral traits can be explained in relation (...) to evolutionary history in biological organisms, in synthetic life forms behavior depends on a design driven by a research agenda, further shaped by broader human goals. This point will be illustrated with a case study on a synthetic life form. Consequently, the putative epistemic benefit of reaching a better understanding of behavioral autonomy in biological organisms by synthesizing artificial life forms is subject to doubt: the autonomy observed in such artificial organisms may be a mere projection of human agency. Further questions arise in relation to the need to spell out the relevant human aims when addressing potential social or ethical implications of synthesizing artificial life forms. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently (...) interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

Recently, I argued that human subjects in artificial wombs (AWs) “share the same moral status as newborns” and so, deserve the same treatment and protections as newborns. This thesis rests on two claims: (A) “Subjects of partial ectogenesis—those that develop in utero for at time before being transferred to AWs—are newborns,” and (B) “Subjects of complete ectogenesis—those who develop in AWs entirely—share the same moral status as newborns.” In response, Elizabeth Chloe Romanis argued that the subject in an AW is (...) “a unique human entity…rather than a fetus or a newborn.” She provides four lines of response to my essay. First, she argues that I have “misconstrued” what birth is. Once we correct that error, it becomes clear that subjects of partial ectogenesis have not been born. Second, she argues that my claims imply that non-implanted embryos (existing in vivo) “would also be ‘born.’” But that is absurd. Third, she claims I fail to “meaningfully respond” to distinctions she draws between subjects of ectogenesis and neonates. Finally, she criticizes my essay for focusing on subjects of AWs rather than focusing on pregnant persons (who should be at the “centre” of debates over AWs). I respond to each of these charges. In doing so, I reaffirm that (contra Romanis) some subjects of ectogenesis are newborns and all subjects of ectogenesis—even those that have not been born—share the same moral status as newborns. (shrink)

Moral reasoning traditionally distinguishes two types of evil:moral (ME) and natural (NE). The standard view is that ME is the product of human agency and so includes phenomena such as war,torture and psychological cruelty; that NE is the product of nonhuman agency, and so includes natural disasters such as earthquakes, floods, disease and famine; and finally, that more complex cases are appropriately analysed as a combination of ME and NE. Recently, as a result of developments in autonomous agents in cyberspace, (...) a new class of interesting and important examples of hybrid evil has come to light. In this paper, it is called artificial evil (AE) and a case is made for considering it to complement ME and NE to produce a more adequate taxonomy. By isolating the features that have led to the appearance of AE, cyberspace is characterised as a self-contained environment that forms the essential component in any foundation of the emerging field of Computer Ethics (CE). It is argued that this goes someway towards providing a methodological explanation of why cyberspace is central to so many of CE's concerns; and it is shown how notions of good and evil can be formulated in cyberspace. Of considerable interest is how the propensity for an agent's action to be morally good or evil can be determined even in the absence of biologically sentient participants and thus allows artificial agents not only to perpetrate evil (and fort that matter good) but conversely to `receive' or `suffer from' it. The thesis defended is that the notion of entropy structure, which encapsulates human value judgement concerning cyberspace in a formal mathematical definition, is sufficient to achieve this purpose and, moreover, that the concept of AE can be determined formally, by mathematical methods. A consequence of this approach is that the debate on whether CE should be considered unique, and hence developed as a Macroethics, may be viewed, constructively,in an alternative manner. The case is made that whilst CE issues are not uncontroversially unique, they are sufficiently novel to render inadequate the approach of standard Macroethics such as Utilitarianism and Deontologism and hence to prompt the search for a robust ethical theory that can deal with them successfully. The name Information Ethics (IE) is proposed for that theory. Itis argued that the uniqueness of IE is justified by its being non-biologically biased and patient-oriented: IE is an Environmental Macroethics based on the concept of data entity rather than life. It follows that the novelty of CE issues such as AE can be appreciated properly because IE provides a new perspective (though not vice versa). In light of the discussion provided in this paper, it is concluded that Computer Ethics is worthy of independent study because it requires its own application-specific knowledge and is capable of supporting a methodological foundation, Information Ethics. (shrink)

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

The application of artificial intelligence (AI) to judicial decision-making has already begun in many jurisdictions around the world. While AI seems to promise greater fairness, access to justice, and legal certainty, issues of discrimination and transparency have emerged and put liberal democratic principles under pressure, most notably in the context of bail decisions. Despite this, there has been no systematic analysis of the risks to liberal democratic values from implementing AI into judicial decision-making. This article sets out to fill this (...) void by identifying and engaging with challenges arising from artificial judicial decision-making, focusing on three pillars of liberal democracy, namely equal treatment of citizens, transparency, and judicial independence. Methodologically, the work takes a comparative perspective between human and artificial decision-making, using the former as a normative benchmark to evaluate the latter. -/- The chapter first argues that AI that would improve on equal treatment of citizens has already been developed, but not yet adopted. Second, while the lack of transparency in AI decision-making poses severe risks which ought to be addressed, AI can also increase the transparency of options and trade-offs that policy makers face when considering the consequences of artificial judicial decision-making. Such transparency of options offers tremendous benefits from a democratic perspective. Third, the overall shift of power from human intuition to advanced AI may threaten judicial independence, and with it the separation of powers. While improvements regarding discrimination and transparency are available or on the horizon, it remains unclear how judicial independence can be protected, especially with the potential development of advanced artificial judicial intelligence (AAJI). Working out the political and legal infrastructure to reap the fruits of artificial judicial intelligence in a safe and stable manner should become a priority of future research in this area. (shrink)

This paper investigates the nature of dispositional properties in the context of artificial intelligence systems. We start by examining the distinctive features of natural dispositions according to criteria introduced by McGeer (2018) for distinguishing between object-centered dispositions (i.e., properties like ‘fragility’) and agent-based abilities, including both ‘habits’ and ‘skills’ (a.k.a. ‘intelligent capacities’, Ryle 1949). We then explore to what extent the distinction applies to artificial dispositions in the context of two very different kinds of artificial systems, one based on rule-based (...) classical logic and the other on reinforcement learning. Here we defend three substantive claims. First, we argue that artificial systems are not equal in the kinds of dispositional properties they instantiate. In particular, we show that logical systems instantiate merely object-centered dispositions whereas reinforcement learning systems allow for the instantiation of agent-based abilities. Second, we explore the similarities and differences between the agent-centered abilities of artificial systems and those of humans, especially as relates to the important distinction made in the human case between habits and skills/intelligent capacities. The upshot is that the agent-centered abilities of truly intelligent artificial systems are distinctive enough to constitute a third type of agent-based ability — blended agent-based ability — raising substantial questions as to how we understand the nature of their agency. Third, we explore one aspect of this problem, focussing on whether systems of this type are properly considered ‘responsible agents’, at least in some contexts and for some purposes. The ramifications of our analysis will turn out to be directly relevant to various ethical concerns of artificial intelligence. (shrink)

Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, we (...) critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. (shrink)

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes (...) clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness. (shrink)

In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...) willing to consider robots as artists than humans, which is partially explained by the fact that they are less disposed to attribute artistic intentions to robots. (shrink)

In the field of machine consciousness, it has been argued that in order to build human-like conscious machines, we must first have a computational model of qualia. To this end, some have proposed a framework that supports qualia in machines by implementing a model with three computational areas (i.e., the subconceptual, conceptual, and linguistic areas). These abstract mechanisms purportedly enable the assessment of artificial qualia. However, several critics of the machine consciousness project dispute this possibility. For instance, Searle, in his (...) Chinese room objection, argues that however sophisticated a computational system is, it can never exhibit intentionality; thus, would also fail to exhibit consciousness or any of its varieties. This paper argues that the proposed architecture mentioned above answers the problem posed by Searle, at least in part. Specifically, it argues that we could reformulate Searle’s worries in the Chinese room in terms of the three-stage artificial qualia model. And by doing so, we could see that the person doing all the translations in the room could realize the three areas in the proposed framework. Consequently, this demonstrates the actualization of self-consciousness in machines. (shrink)

Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims to: (i) (...) defend the need for a novel conceptual model for understanding the systemic legal disruption caused by new technologies such as AI; (ii) to situate this model in relation to preceding debates about the interaction of regulation with new technologies (particularly the ‘cyberlaw’ and ‘robolaw’ debates); and (iii) to set out a detailed model for understanding the legal disruption precipitated by AI, examining both pathways stemming from new affordances that can give rise to a regulatory ‘disruptive moment’, as well as the Legal Development, Displacement or Destruction that can ensue. The article proposes that this model of legal disruption can be broadly generalisable to understanding the legal effects and challenges of other emerging technologies. (shrink)

This paper investigates the claim that artificial Intelligence Systems cannot be held morally responsible because they do not have an ability for agential self-awareness e.g. they cannot be aware that they are the agents of an action. The main suggestion is that if agential self-awareness and related first person representations presuppose an awareness of a self, the possibility of responsible artificial intelligence systems cannot be evaluated independently of research conducted on the nature of the self. Focusing on a specific account (...) of the self from the phenomenological tradition, this paper suggests that a minimal necessary condition that artificial intelligence systems must satisfy so that they have a capability for self-awareness, is having a minimal self defined as ‘a sense of ownership’. As this sense of ownership is usually associated with having a living body, one suggestion is that artificial intelligence systems must have similar living bodies so they can have a sense of self. Discussing cases of robotic animals as examples of the possibility of artificial intelligence systems having a sense of self, the paper concludes that the possibility of artificial intelligence systems having a ‘sense of ownership’ or a sense of self may be a necessary condition for having responsibility. (shrink)

There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. We thus (...) designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity. (shrink)

While feminist critiques of AI are increasingly common in the scholarly literature, they are by no means new. Alison Adam’s Artificial Knowing (1998) brought a feminist social and epistemological stance to the analysis of AI, critiquing the symbolic AI systems of her day and proposing constructive alternatives. In this paper, we seek to revisit and renew Adam’s arguments and methodology, exploring their resonances with current feminist concerns and their relevance to contemporary machine learning. Like Adam, we ask how new AI (...) methods could be adapted for feminist purposes and what role new technologies might play in addressing concerns raised by feminist epistemologists and theorists about algorithmic systems. In particular, we highlight distributed and federated learning as providing partial solutions to the power-oriented concerns that have stymied efforts to make machine learning systems more representative and pluralist. (shrink)

En On the Origin of Species Darwin distingue explícitamente entre tres tipos de selección: la selección natural, la artificial y la sexual. En este trabajo, a partir de un estudio más sistemático que historiográfico, se intenta encontrar la relación entre estos tres tipos de selección en la obra de Darwin. Si bien la distinción entre estos distintos mecanismos es de suma importancia en la obra de Darwin, la tesis de este trabajo es que tanto la selección artificial como la sexual (...) no son mecanismos distintos de la selección natural. Particularmente se sostiene que la selección artificial y la sexual son casos de la natural, y por esto sirven para su contrastación. (shrink)

The article reflects on where AI is headed and the world along with it, considering trust, ethics and safety. Implicit in artificial thinking and doomsday appraisals is the engineered divorce from reality of sublime human embodiment. Jeffrey White, Dietrich Brandt, Jan Soeffner, and Larry Stapleton, four scholars associated with AI & Society, address these issues, and more, in the following exchange.

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind of possibility (...) by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is. (shrink)

Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents, it has been proven difficult to approach from a computational perspective. In this work, we present the first technical implementation of artificial virtuous agents in moral simulations. First, we review previous conceptual and technical work in artificial virtue ethics and describe a functionalistic path to AVAs based on dispositional virtues, bottom-up learning, and top-down eudaimonic reward. We then provide the details of a (...) technical implementation in a moral simulation based on a tragedy of the commons scenario. The experimental results show how the AVAs learn to tackle cooperation problems while exhibiting core features of their theoretical counterpart, including moral character, dispositional virtues, learning from experience, and the pursuit of eudaimonia. Ultimately, we argue that virtue ethics provides a compelling path toward morally excellent machines and that our work provides an important starting point for such endeavors. (shrink)

Some of the systems used in natural language generation (NLG), a branch of applied computational linguistics, have the capacity to create or assemble somewhat original messages adapted to new contexts. In this paper, taking Bernard Williams’ account of assertion by machines as a starting point, I argue that NLG systems meet the criteria for being speech actants to a substantial degree. They are capable of authoring original messages, and can even simulate illocutionary force and speaker meaning. Background intelligence embedded in (...) their datasets enhances these speech capacities. Although there is an open question about who is ultimately responsible for their speech, if anybody, we can settle this question by using the notion of proxy speech, in which responsibility for artificial speech acts is assigned legally or conventionally to an entity separate from the speech actant. (shrink)

This paper explores the theological and philosophical implications of artificial intelligence (AI) and Neuroscience research on the Christian’s notion of the human person. The paschal mystery of Christ is the intuitive foundation of Christian anthropology. In the intellectual history of the Christianity, Platonism and Aristotelianism have been employed to articulate the Christian philosophical anthropology. The Aristotelian systematization has endured to this era. Since the modern period of the Western intellectual history, Aristotelianism has been supplanted by the positive sciences as the (...) standard scientific and philosophical interpretative framework for many. This paper argues that there is need for a new Christian anthropology based on the interpretation of the paschal mystery of Christ that is consistent with contemporary science and philosophy, especially as informed by research in AI and neuroscience. Christian anthropology today ought to be fully aware of the theological and philosophical implications of AI and neuroscience research. This paper denotes this new Christian anthropology as neuro-theological Christian anthropology. (shrink)

This paper argues that there is an urgent need for an international doctrine for cyberspace skirmishes before they escalate into conventional warfare.

Artificial agents (AAs), particularly but not only those in Cyberspace, extend the class of entities that can be involved in moral situations. For they can be conceived of as moral patients (as entities that can be acted upon for good or evil) and also as moral agents (as entities that can perform actions, again for good or evil). In this paper, we clarify the concept of agent and go on to separate the concerns of morality and responsibility of agents (most (...) interestingly for us, of AAs). We conclude that there is substantial and important scope, particularly in Computer Ethics, for the concept of moral agent not necessarily exhibiting free will, mental states or responsibility. This complements the more traditional approach, common at least since Montaigne and Descartes, which considers whether or not (artificial) agents have mental states, feelings, emotions and so on. By focussing directly on mind-less morality we are able to avoid that question and also many of the concerns of Artificial Intelligence. A vital component in our approach is the Method of Abstraction for analysing the level of abstraction (LoA) at which an agent is considered to act. The LoA is determined by the way in which one chooses to describe, analyse and discuss a system and its context. The Method of Abstraction is explained in terms of an interface or set of features or observables at a given LoA. Agenthood, and in particular moral agenthood, depends on a LoA. Our guidelines for agenthood are: interactivity (response to stimulus by change of state), autonomy (ability to change state without stimulus) and adaptability (ability to change the transition rules by which state is changed) at a given LoA. Morality may be thought of as a threshold defined on the observables in the interface determining the LoA under consideration. An agent is morally good if its actions all respect that threshold; and it is morally evil if some action violates it. That view is particularly informative when the agent constitutes a software or digital system, and the observables are numerical. Finally we review the consequences for Computer Ethics of our approach. In conclusion, this approach facilitates the discussion of the morality of agents not only in Cyberspace but also in the biosphere, where animals can be considered moral agents without their having to display free will, emotions or mental states, and in social contexts, where systems like organizations can play the role of moral agents. The primary cost of this facility is the extension of the class of agents and moral agents to embrace AAs. (shrink)

In most domains of human life, we are willing to accept that there are experts with greater knowledge and competencies that distinguish them from non-experts or laypeople. Despite this fact, the very recognition of expertise curiously becomes more controversial in the case of “moral experts”. Do moral experts exist? And, if they indeed do, are there ethical reasons for us to follow their advice? Likewise, can emerging technological developments broaden our very concept of moral expertise? In this article, we begin (...) by arguing that the objections that have tried to deny the existence (and convenience) of moral expertise are unsatisfactory. After that, we show that people have ethical reasons to ask for a piece of moral advice in daily life situations. Then, we argue that some Artificial Intelligence (AI) systems can play an increasing role in human morality by becoming moral experts. Some AI-based moral assistants can qualify as artificial moral experts and we would have good ethical reasons to use them. (shrink)

In this paper, we defend three claims about what it will take for an AI system to be a basic moral patient to whom we can owe duties of non-maleficence not to harm her and duties of beneficence to benefit her: (1) Moral patients are mental patients; (2) Mental patients are true intentional systems; and (3) True intentional systems are systematically flexible. We suggest that we should be particularly alert to the possibility of such systematically flexible true intentional systems developing (...) in the areas of exploratory robots and artificial personal assistants. Finally, we argue that in light of our failure to respect the well-being of existing biological moral patients and worries about our limited resources, there are compelling moral reasons to treat artificial moral patiency as something to be avoided at least for now. (shrink)

Recent progress in artificial intelligence (AI) has drawn attention to the technology’s transformative potential, including what some see as its prospects for causing large-scale harm. We review two influential arguments purporting to show how AI could pose catastrophic risks. The first argument — the Problem of Power-Seeking — claims that, under certain assumptions, advanced AI systems are likely to engage in dangerous power-seeking behavior in pursuit of their goals. We review reasons for thinking that AI systems might seek power, that (...) they might obtain it, that this could lead to catastrophe, and that we might build and deploy such systems anyway. The second argument claims that the development of human-level AI will unlock rapid further progress, culminating in AI systems far more capable than any human — this is the Singularity Hypothesis. Power-seeking behavior on the part of such systems might be particularly dangerous. We discuss a variety of objections to both arguments and conclude by assessing the state of the debate. (shrink)

This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artifi-cial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or utilitarianism, (...) that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelli-gence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies. (shrink)

Computer assisted theorem proving is an increasingly important part of mathematical methodology, as well as a long-standing topic in artificial intelligence (AI) research. However, the current generation of theorem proving software have limited functioning in terms of providing new proofs. Importantly, they are not able to discriminate interesting theorems and proofs from trivial ones. In order for computers to develop further in theorem proving, there would need to be a radical change in how the software functions. Recently, machine learning results (...) in solving mathematical tasks have shown early promise that deep artificial neural networks could learn symbolic mathematical processing. In this paper, I analyze the theoretical prospects of such neural networks in proving mathematical theorems. In particular, I focus on the question how such AI systems could be incorporated in practice to theorem proving and what consequences that could have. In the most optimistic scenario, this includes the possibility of autonomous automated theorem provers (AATP). Here I discuss whether such AI systems could, or should, become accepted as active agents in mathematical communities. (shrink)

La Inteligencia Artificial (IA) ha supuesto un gran avance para la humanidad en diversos campos; sin embargo, eso no implica que su actividad esté exenta de reflexión ética. La humanidad está enfrentado, y va a enfrentar en el futuro, numerosos desafíos que van a obligar a elaborar nuevas ideas para poder vivir a la altura de los tiempos. Entre esos desafíos encontramos el laboral y económico, el de mejoramiento humano, el militar y de seguridad y el político y jurídico, entre (...) otros. Así pues, una vez considerados los desafíos en el terreno de la IA, una referencia ética que puede servir para enfrentar dichos desafíos, puede ser el principio de responsabilidad de Hans Jonas. La lectura de este aporte ético puede facilitarnos unas primeras coordenadas para la orientación en medio de un horizonte de posibilidades nuevo para la humanidad y, adicionalmente, servir como punto de partida en el compromiso que deben asumir los diferentes saberes implicados en este novedoso campo. (shrink)

There is a meaning crisis correlated with the exponential rate of technology; As it disrupts our lives and forces society and its individuals to adapt increasingly quickly, we lose meaning. The challenge to deal with this immediately becomes philosophical, as we have never before needed to define and understand what “meaning” is on such a conscious level.

This paper argues that to negate the ontological difference between the natural and the artificial, is not plausible; nor is the reduction of the natural to the artificial or vice versa possible. Except if one intends to empty the semantic content of the terms and notions: “natural” and “artificial.” Most philosophical discussions on Artificial Intelligence (AI) have always been in relation to the human person, especially as it relates to human intelligence, consciousness and/or mind in general. This paper, intends to (...) broaden the conversation, by discussing AI in relation to the notions of “nature” and the “artificial.” This intention is to more critically understand the artificiality in and of artificial intelligence. To achieve this, the notion of “nature” in Aristotle’s Philosophy of Nature, has been employed as an epistemological tool in interrogating the notion of the artificial and the objectives of the science and technology of Artificial Intelligence. (shrink)

It is widely agreed that possession of consciousness contributes to an entity’s moral status, even if it is not necessary for moral status (Levy and Savulescu 2009). An entity is considered to have...

AI, especially in the case of Deepfakes, has the capacity to undermine our confidence in the original, genuine, authentic nature of what we see and hear. And yet digital technologies, in the form of databases and other detection tools also make it easier to spot forgeries and to establish the authenticity of a work. Using the notion of ectypes, this paper discusses current conceptions of authenticity and reproduction and examines how, in the future, these might be adapted for use in (...) the digital sphere. (shrink)

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence, entitled ‘New Generation Artificial Intelligence Development Plan’. This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on (...) the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents. (shrink)

Whether causing flash crashes in financial markets, purchasing illegal drugs, or running over pedestrians, AI is increasingly engaging in activity that would be criminal for a natural person, or even an artificial person like a corporation. We argue that criminal law falls short in cases where an AI causes certain types of harm and there are no practically or legally identifiable upstream criminal actors. This Article explores potential solutions to this problem, focusing on holding AI directly criminally liable where it (...) is acting autonomously and irreducibly. Conventional wisdom holds that punishing AI is incongruous with basic criminal law principles such as the capacity for culpability and the requirement of a guilty mind. -/- Drawing on analogies to corporate and strict criminal liability, as well as familiar imputation principles, we show how a coherent theoretical case can be constructed for AI punishment. AI punishment could result in general deterrence and expressive benefits, and it need not run afoul of negative limitations such as punishing in excess of culpability. Ultimately, however, punishing AI is not justified, because it might entail significant costs and it would certainly require radical legal changes. Modest changes to existing criminal laws that target persons, together with potentially expanded civil liability, are a better solution to AI crime. (shrink)

There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that structure their development. (...) This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings. (shrink)