Can humans and artificial intelligences share concepts and communicate? Making AI Intelligible shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. (...) Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (for example, creditworthiness, recidivism, cancer, and combatants). If AIs can share our concepts, that will go some way towards justifying this reliance on AI. This ground-breaking study offers insight into how to take some first steps towards achieving Interpretable AI. (shrink)

One of the strands of the Transhumanist movement, Singulitarianism, studies the possibility that high-level artificial intelligence may be created in the future, debating ways to ensure that the interaction between human society and advanced artificial intelligence can occur safely and beneficially. But how can we guarantee this safe interaction? Are there any indications that a Singularity may be on the horizon? In trying to answer these questions, We'll make a small introduction to the area of security research in artificial intelligence. (...) We'll review some of the current paradigms in the development of autonomous intelligent systems and evidence that we can use to prospect the coming of a possible technological Singularity. Finally, we will present a reflection using the COVID-19 pandemic, something that showed that our biggest problem in managing existential risks is our lack of coordination skills as a global society. (shrink)

The international debate on the ethics and legality of autonomous weapon systems (AWS) as well as the call for a ban are primarily focused on the nebulous concept of fully autonomous AWS. More specifically, on AWS that are capable of target selection and engagement without human supervision or control. This thesis argues that such a conception of autonomy is divorced both from military planning and decision-making operations as well as the design requirements that govern AWS engineering and subsequently the tracking (...) and tracing of moral responsibility. To do this, this thesis marries two different levels of meaningful human control (MHC), termed levels of abstraction, to couple military operations with design ethics. In doing so, this thesis argues that the contentious notion of ‘full’ autonomy is not problematic under this two-tiered understanding of MHC. It proceeds to propose the value sensitive design (VSD) approach as a means for designing for MHC. (shrink)

The aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and (...) a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical. (shrink)

In this chapter, the focus is on robotics development and its ethical implications, especially on some particular applications or interaction principles. In recent years, such developments have happened very quickly, based on the advances achieved in the last few decades in industrial robotics. The technological developments in manufacturing, with the implementation of Industry 4.0 strategies in most industrialized countries, and the dissemination of production strategies into services and health sectors, enabled robotics to develop in a variety of new directions. Policy (...) making and ethical awareness addressed these issues using socio-economic knowledge and also in an effort to solve some of the application problems raised in a range of different circumstances and sectoral environments. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

With the fast pace of AI development, the problem of preventing its global catastrophic risks arises. However, no satisfactory solution has been found. From several possibilities, the confinement of AI in a box is considered as a low-quality possible solution for AI safety. However, some treacherous AIs can be stopped by effective confinement if it is used as an additional measure. Here, we proposed an idealized model of the best possible confinement by aggregating all known ideas in the field of (...) AI boxing. We model the confinement based on the principles used in the safety engineering of nuclear power plants. We show that AI confinement should be implemented in several levels of defense. These levels include 1) AI design in fail-safe manner 2) limiting its capabilities, preventing self-improving and circuit breakers on treacherous turn 3) isolation from the outside world and, lastly, as the final hope 4) outside measures oriented on stopping AI in the wild. We demonstrate that the substantial number (more than 50 ideas listed in the article) of mutually independent measures could provide a relatively high probability of the containment of a human-level AI but may be not sufficient to preserve runaway of superintelligent AI. Thus, these measures will work only if they are used to prevent superintelligent AI creation, but not for containing superintelligence. We suggest that there could be a safe operation threshold, on which AI is useful, but is not able to hack containment system from the inside, the same way as a safe level of chain reaction inside nuclear power plants is maintained. However, overall, a failure of the confinement is inevitable, so we need to use the full AGI limited number of times (AI-ticks). (shrink)

Alan Turing, one of the fathers of computing, warned that Artificial Intelligence (AI) could one day pose an existential risk to humanity. Today, recent advancements in the field AI have been accompanied by a renewed set of existential warnings. But what exactly constitutes an existential risk? And how exactly does AI pose such a threat? In this chapter we aim to answer these questions. In particular, we will critically explore three commonly cited reasons for thinking that AI poses an existential (...) threat to humanity: the control problem, the possibility of global disruption from an AI race dynamic, and the weaponization of AI. (shrink)

According to some philosophers, if moral agency is understood in behaviourist terms, robots could become moral agents that are as good as or even better than humans. Given the behaviourist conception, it is natural to think that there is no interesting moral difference between robots and humans in terms of moral agency (call it the _equivalence thesis_). However, such moral differences exist: based on Strawson’s account of participant reactive attitude and Scanlon’s relational account of blame, I argue that a distinct (...) kind of reason available to humans—call it _human-relative reason_—is not available to robots. The difference in moral reason entails that sometimes an action is morally permissible for humans, but not for robots. Therefore, when developing moral robots, we cannot consider only what humans can or cannot do. I use examples of paternalism to illustrate my argument. (shrink)

Some authors have recently suggested that it is time to consider rights for robots. These suggestions are based on the claim that the question of robot rights should not depend on a standard set of conditions for ‘moral status’; but instead, the question is to be framed in a new way, by rejecting the is/ought distinction, making a relational turn, or assuming a methodological behaviourism. We try to clarify these suggestions and to show their highly problematic consequences. While we find (...) the suggestions ultimately unmotivated, the discussion shows that our epistemic condition with respect to the moral status of others does raise problems, and that the human tendency to empathise with things that do not have moral status should be taken seriously—we suggest that it produces a “derived moral status”. Finally, it turns out that there is typically no individual in real AI that could even be said to be the bearer of moral status. Overall, there is no reason to think that robot rights are an issue now. (shrink)

This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve gold-standard societal (...) values, such as fairness, freedom and long-term sustainability. This challenge of integrating the AIR literature was matched by the identification of a structural common ground among different approaches. The AIR framework results from an effort to identify and later analytically deduce synthetic, and generic tool for a country-specific, stakeholder-aware analysis of AIR matters. Theories and principles as diverse as Agile and Ethics were combined in the “AIR framework”, which provides a conceptual lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the AIR framework serves as a theoretically sound starting point for endeavours related to AI regulation, from legislation to research and development. As we know, the (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds based on contemporary governance techniques, and social values legitimated via dialogue and scientific research. (shrink)

Increasingly, innovation in artificial intelligence technologies portends the re-conceptualization of human existentiality along the paradigm of posthumanism. An exposition of this through a critical culturo-historical methodology uncloaks the Eurocentric genitive basis of the philosophical anthropology that underpins this technological posthumanism, as well as its dystopian possibilities. As a contribution to obviating the latter, an Africanist civilizational humanism proclaimed by Pixley ka Isaka Seme is proffered as a plausible alternative paradigm for humanity’s technological advancement. Seme, a pan-Africanist thinker of the early (...) twentieth century, proclaimed humanistic-spirituality as the indispensable gift African Civilisation-in-its-renaissance is yet to offer global humanity. His postulation is being provided as a contribution to the archive on cross-cultural ethics of artificial intelligence. (shrink)

There may be a limit on our capacity to suppress anthropocentric tendencies toward non-human others. Normally, we do not reach this limit in our dealings with animals, the environment, etc. Thus, continued striving to overcome anthropocentrism when confronted with these non-human others may be justified. Anticipation of super artificial intelligence may force us to face this limit, denying us the ability to free ourselves completely of anthropocentrism. This could be for our own good.

This paper reflects on the problem of endowing Artificial Intelligence with legal subjectivity, especially with regard to civil law. It is necessary to reject the myth that the criteria of legal subjectivity are sentience and reason. Arguing that AI may have potential legal subjectivity based on an analogy to animals or juristic persons suggests the existence of a single hierarchy or sequence of entities, organized according to their degree of similarity to human beings; also, that the place of an entity (...) in this hierarchy determines the scope of subjectivity attributed to it. Rather, it is participation or presence in social life, whatever the role, that is the true criterion of subjectivity. In addition, it is clear that even if AI is not currently a significant participant in social life, it will be in the nearest future. Despite the potential dangers associated with endowing AI with some kind of subjectivity, such a course is inescapable, and should be considered sooner rather than later. (shrink)

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...) the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

This paper explores some ways in which artificial intelligence (AI) could be used to improve human moral judgments in bioethics by avoiding some of the most common sources of error in moral judgment, including ignorance, confusion, and bias. It surveys three existing proposals for building human morality into AI: Top-down, bottom-up, and hybrid approaches. Then it proposes a multi-step, hybrid method, using the example of kidney allocations for transplants as a test case. The paper concludes with brief remarks about how (...) to handle several complications, respond to some objections, and extend this novel method to other important moral issues in bioethics and beyond. (shrink)

The field of AI ethics during the current and previous decade is receiving an increasing amount of attention from all involved stakeholders: the public, science, philosophy, religious organizations, enterprises, governments, and various organizations. However, this field currently lacks consensus on scope, ethico-philosophical foundations, or common methodology. This thesis aims to contribute towards filling this gap by providing an answer to the two main research questions: first, what theory can explain moral scenarios in which AI entities are participants?; and second, what (...) theory can explain the process of moral reasoning, decision and action, for AI entities in virtual, simulated and real-life moral scenarios? This thesis answers these two research questions with its two main contributions to the field of AI ethics, a substantial and a methodological contribution. The substantial contribution is a coherent and novel theory named Ethics of Systems Framework, as well as a possible inception of a new field of study: ethics of systems. The methodological contribution is the creation of its main methodological tool, the Ethics of Systems Interface. The second part of the research effort was focused on testing and demonstrating the capacities of the Ethics of Systems Framework and Interface in modeling and managing moral scenarios in which AI and other entities participate. Further work can focus on building on top of the foundations of the Framework provided here, increasing the scope of moral theories and simulated scenarios, improving the level of detail and parameters to reflect real-life situations, and field-testing the Framework on actual AI systems. (shrink)

This essay interrogates the common assumption that good care is necessarily human care. It looks to disruptive fictional representations of robot care to assist its development of a theory of posthuman care that jettisons the implied anthropocentrism of ethics of care philosophy but retains care’s foregrounding of entanglement, embodiment and obligation. The essay reads speculative representations of robot care, particularly the Swedish television programme Äkta människor (Real Humans), alongside ethics of care philosophy and critical posthumanism to highlight their synergetic critiques (...) of neoliberal affective economies and humanist hierarchies that treat some bodies and affects as more real than others. These texts and discourses assist me in proposing a theory of care that regards vulnerability as the normative effect of posthuman vital embodiment, as opposed to an anomalous state that can be overcome or corrected via neoliberal practice. (shrink)

Suppose you want to do as much good as possible. What should you do? According to members of the effective altruism movement—which has produced much of the thinking on this issue and counts several moral philosophers as its key protagonists—we should prioritise among the world’s problems by assessing their scale, solvability, and neglectedness. Once we’ve done this, the three top priorities, not necessarily in this order, are (1) aiding the world’s poorest people by providing life-saving medical treatments or alleviating poverty (...) itself, (2) preventing global catastrophic risks, such as those posed by nuclear war or rogue artificial intelligence, and (3) ending factory farming. These claims are both plausible and striking. If correct, they should prompt a stark revision of how we approach our altruistic activities. However, the project of determining how to do the most good—as opposed to say, whether we should do the most good—has only recently, within the last ten years, become the subject of serious academic attention. Many key claims have not yet been carefully scrutinised. This is a cause for concern: are effective altruists doing good badly? In this thesis, I critique and develop some of the latest claims about how individuals can do the most good. I do this in three areas: the value of saving lives (preventing premature deaths), how best to improve lives (making people happier during their lives), and cause prioritisation methodology (frameworks for determining which problems are the highest priorities). In each case, I raise novel theoretical considerations that, when incorporated, change the analysis. Roughly speaking, my main conclusions are (1) saving lives is not as straightforwardly good we tend to suppose, may not be good at all, and is not clearly a priority; (2) happiness can be measured through self-reports and, based on the self-reported evidence, treating mental health stands out as an overlooked problem that may be an even more cost-effective way to improve lives than alleviating poverty; (3) the cause prioritisation methodology proposed by effective altruists needs to be moderately reconceptualised and, when it is, it turns out it is not as illuminating a tool as we might have thought and hoped. (shrink)

As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a comprehensive analysis (...) of the relevant arguments for and against creating AMAs, and we argue that all things considered we have strong reasons to continue to responsibly develop AMAs. The key contributions of this paper are threefold. First, to provide the first comprehensive response to the important arguments made against AMAs by Wynsberghe and Robbins (in “Critiquing the Reasons for Making Artificial Moral Agents”, Science and Engineering Ethics 25, 2019) and to introduce several novel lines of argument in the process. Second, to collate and thematise for the first time the key arguments for and against AMAs in a single paper. Third, to recast the debate away from blanket arguments for or against AMAs in general, to a more nuanced discussion about the use of what sort of AMAs, in what sort of contexts, and for what sort of purposes is morally appropriate. (shrink)

This article investigates religious ideals persistent in the datafication of information society. Its nodal point is Thomas Bayes, after whom Laplace names the primal probability algorithm. It reconsiders their mathematical innovations with Laplace's providential deism and Bayes' singular theological treatise. Conceptions of divine justice one finds among probability theorists play no small part in the algorithmic data-mining and microtargeting of Cambridge Analytica. Theological traces within mathematical computation are emphasized as the vantage over large numbers shifts to weights beyond enumeration in (...) probability theory. Collateral secularizations of predestination and theodicy emerge as probability optimizes into Bayesian prediction and machine learning. The paper revisits the semiotics and theism of Peirce and a given beyond the probable in Whitehead to recontextualize the critiques of providence by Agamben and Foucault. It reconsiders datafication problems alongside Nietzschean valuations. Religiosity likely remains encoded within the very algorithms presumed purified by technoscientific secularity or mathematical dispassion. (shrink)

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements (...) in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified. (shrink)

Human values seem to vary across time and space. What implications does this have for the future of human value? Will our human and (perhaps) post-human offspring have very different values from our own? Can we study the future of human values in an insightful and systematic way? This article makes three contributions to the debate about the future of human values. First, it argues that the systematic study of future values is both necessary in and of itself and an (...) important complement to other future-oriented inquiries. Second, it sets out a methodology and a set of methods for undertaking this study. Third, it gives a practical illustration of what this ‘axiological futurism’ might look like by developing a model of the axiological possibility space that humans are likely to navigate over the coming decades. (shrink)

I often hear people say about children: “They learn like a sponge.” It is clear young brains have a lot more neuroplasticity, which makes it easier for them to learn. But we know from decades of research on neuroplasticity, the capacity for the brain to change and adapt to new situations is there for a lifetime.

This paper considers the hotly debated issue of whether one should grant moral and legal personhood to intelligent robots once they have achieved a certain standard of sophistication based on such criteria as rationality, autonomy, and social relations. The starting point for the analysis is the European Parliament’s resolution on Civil Law Rules on Robotics and its recommendation that robots be granted legal status and electronic personhood. The resolution is discussed against the background of the so-called Robotics Open Letter, which (...) is critical of the Civil Law Rules on Robotics. The paper reviews issues related to the moral and legal status of intelligent robots and the notion of legal personhood, including an analysis of the relation between moral and legal personhood in general and with respect to robots in particular. It examines two analogies, to corporations and animals, that have been proposed to elucidate the moral and legal status of robots. The paper concludes that one should not ascribe moral and legal personhood to currently existing robots, given their technological limitations, but that one should do so once they have achieved a certain level at which they would become comparable to human beings. (shrink)

What does Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek a dialogue in which the reader may inquire about the future and the ethical and social problems that our technological advance may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and (...) debated, and several ethical principles and guides have been suggested as governance policies for the tech industry. However, would this be the role of AI Ethics? To serve as a soft and ambiguous version of the law? I would like to promote in this article a more Cyberpunk way of doing AI Ethics, whit a more anarchic way of governance. In this study, I will seek to expose some of the deficits of the underlying power structures of our society, and suggest that AI governance be subject to public opinion, so that ‘good AI’ can become ‘good AI for all’. (shrink)

Nicholas Agar, Jeff McMahan and Allen Buchanan have all expressed concerns about enhancing humans far outside the species-typical range. They argue radically enhanced beings will be entitled to greater and more beneficial treatment through an enhanced moral status, or a stronger claim to basic rights. I challenge these claims by first arguing that emerging technologies will likely give the enhanced direct control over their mental states. The lack of control we currently exhibit over our mental lives greatly contributes to our (...) sense of vulnerability. I then argue moral status should be viewed in terms of vulnerability. The enhanced will slowly gain the ability to command their mental states, reducing their vulnerability. These radically enhanced beings will have greater capacities, and possibly an inner life more valuable than our own. They will also be less vulnerable, and as a result, their moral status will be subordinate to our own. (shrink)

In the fields of artificial intelligence and robotics, the term “autonomy” is generally used to mean the capacity of an artificial agent to operate independently of human guidance. It is thereby assumed that the agent has a fixed goal or “utility function” with respect to which the appropriateness of its actions will be evaluated. From a philosophical perspective, this notion of autonomy seems oddly weak. For, in philosophy, the term is generally used to refer to a stronger capacity, namely the (...) capacity to “give oneself the law,” to decide by oneself what one’s goal or principle of action will be. The predominant view in the literature on the long-term prospects and risks of artificial intelligence is that an artificial agent cannot exhibit such autonomy because it cannot rationally change its own final goal, since changing the final goal is counterproductive with respect to that goal and hence undesirable. The aim of this paper is to challenge this view by showing that it is based on questionable assumptions about the nature of goals and values. I argue that a general AI may very well come to modify its final goal in the course of developing its understanding of the world. This has important implications for how we are to assess the long-term prospects and risks of artificial intelligence. (shrink)

In a landmark essay, Andreas Matthias claimed that current developments in autonomous, artificially intelligent systems are creating a so-called responsibility gap, which is allegedly ever-widening and stands to undermine both the moral and legal frameworks of our society. But how severe is the threat posed by emerging technologies? In fact, a great number of authors have indicated that the fear is thoroughly instilled. The most pessimistic are calling for a drastic scaling-back or complete moratorium on AI systems, while the optimists (...) aim to show that the gap can be bridged nonetheless. Contrary to both camps, I argue against the prevailing assumption that there is a technology-based responsibility gap. I show how moral responsibility is a dynamic and flexible process, one that can effectively encompass emerging technological entities. (shrink)

Whether it would take one decade or several centuries, many agree that it is possible to create a *superintelligence*---an artificial intelligence with a godlike ability to achieve its goals. And many who have reflected carefully on this fact agree that our best hope for a "friendly" superintelligence is to design it to *learn* values like ours, since our values are too complex to program or hardwire explicitly. But the value learning approach to AI safety faces three particularly philosophical puzzles: first, (...) it is unclear how any intelligent system could learn its final values, since to judge one supposedly "final" value against another seems to require a further background standard for judging. Second, it is unclear how to determine the content of a system's values based on its physical or computational structure. Finally, there is the distinctly ethical question of which values we should best aim for the system to learn. I outline a potential answer to these interrelated puzzles, centering on a "miktotelic" proposal for blending a complex, learnable final value out of many simpler ones. (shrink)

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made and (...) used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn. (shrink)

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused on (...) how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

Habermas’s postmetaphysical reading of Kierkegaard is paradigmatic for his understanding of religion. It shows, why Habermas reduces religion to fideism. Therefore the paper reconstructs Habermas’s reception of Kierkegaard and compares it with the accounts of Dieter Henrich and Michael Theunissen. Furthermore it demonstrates how Habermas makes use of Kierkegaard’s dialectics of existence to formulate his postmetaphysical thesis of a cooperative venture.

What kinds of fundamental limits are there in how capable artificial intelligence (AI) systems might become? Two questions in particular are of interest: (1) How much more capable could AI become relative to humans, and (2) how easily could superhuman capability be acquired? To answer these questions, we will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how AI could improve on humans in two major aspects of thought and expertise, namely simulation and (...) pattern recognition. We find that although there are very real limits to prediction, it seems like AI could still substantially improve on human intelligence. (shrink)

If a robot sends a deceptive signal to a human user, is this always and everywhere an unethical act, or might it sometimes be ethically desirable? Building upon previous work in robot ethics, this article tries to clarify and refine our understanding of the ethics of robotic deception. It does so by making three arguments. First, it argues that we need to distinguish between three main forms of robotic deception (external state deception; superficial state deception; and hidden state deception) in (...) order to think clearly about its ethics. Second, it argues that the second type of deception – superficial state deception – is not best thought of as a form of deception, even though it is frequently criticised as such. And third, it argues that the third type of deception is best understood as a form of betrayal because doing so captures the unique ethical harm to which it gives rise, and justifies special ethical protections against its use. (shrink)

The likely near future creation of artificial superintelligence carries significant risks to humanity. These risks are difficult to conceptualise and quantify, but malicious use of existing artificial intelligence by criminals and state actors is already occurring and poses risks to digital security, physical security and integrity of political systems. These risks will increase as artificial intelligence moves closer to superintelligence. While there is little research on risk management tools used in artificial intelligence development, the current global standard for risk management, (...) ISO 31000:2018, is likely used extensively by developers of artificial intelligence technologies. This paper argues that risk management has a common set of vulnerabilities when applied to artificial superintelligence which cannot be resolved within the existing framework and alternative approaches must be developed. Some vulnerabilities are similar to issues posed by malicious threat actors such as professional criminals and terrorists. Like these malicious actors, artificial superintelligence will be capable of rendering mitigation ineffective by working against countermeasures or attacking in ways not anticipated by the risk management process. Criminal threat management recognises this vulnerability and seeks to guide and block the intent of malicious threat actors as an alternative to risk management. An artificial intelligence treachery threat model that acknowledges the failings of risk management and leverages the concepts of criminal threat management and artificial stupidity is proposed. This model identifies emergent malicious behaviour and allows intervention against negative outcomes at the moment of artificial intelligence’s greatest vulnerability. (shrink)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made and used (...) by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

Friendship is an important part of the good life. While many roboticists are eager to create friend-like robots, many philosophers and ethicists are concerned. They argue that robots cannot really be our friends. Robots can only fake the emotional and behavioural cues we associate with friendship. Consequently, we should resist the drive to create robot friends. In this article, I argue that the philosophical critics are wrong. Using the classic virtue-ideal of friendship, I argue that robots can plausibly be considered (...) our virtue friends - that to do so is philosophically reasonable. Furthermore, I argue that even if you do not think that robots can be our virtue friends, they can fulfil other important friendship roles, and can complement and enhance the virtue friendships between human beings. (shrink)

Interactions between an intelligent software agent and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality, we frame these interactions (...) as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction, as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings. (shrink)

There is a non-trivial chance that sometime in the (perhaps somewhat distant) future, someone will build an artificial general intelligence that will surpass human-level cognitive proficiency and go on to become "superintelligent", vastly outperforming humans. The advent of superintelligent AI has great potential, for good or ill. It is therefore imperative that we find a way to ensure-long before one arrives-that any superintelligence we build will consistently act in ways congenial to our interests. This is a very difficult challenge in (...) part because most of the final goals we could give an AI admit of so-called "perverse instantiations". I propose a novel solution to this puzzle: instruct the AI to love humanity. The proposal is compared with Yudkowsky's Coherent Extrapolated Volition, and Bostrom's Moral Modeling proposals. (shrink)

Today’s technological-scientific prospect of posthumanity simultaneously evokes and defies historical understanding. On the one hand, it implies a historical claim of an epochal transformation concerning posthumanity as a new era. On the other, by postulating the birth of a novel, better-than-human subject for this new era, it eliminates the human subject of modern Western historical understanding. In this article, I attempt to understand posthumanity as measured against the story of humanity as the story of history itself. I examine the fate (...) of humanity as the central subject of history in three consecutive steps: first, by exploring how classical philosophies of history achieved the integrity of the greatest historical narrative of history itself through the very invention of humanity as its subject; second, by recounting how this central subject came under heavy criticism by postcolonial and gender studies in the last half-century, targeting the universalism of the story of humanity as the greatest historical narrative of history; and third, by conceptualizing the challenge of posthumanity against both the story of humanity and its criticism. Whereas criticism fragmented history but retained the possibility of smaller-scale narratives, posthumanity does not doubt the feasibility of the story of humanity. Instead, it necessarily invokes humanity, if only in order to be able to claim its supersession by a better-than-human subject. In that, it represents a fundamental challenge to the modern Western historical condition and the very possibility of historical narratives – small-scale or large-scale, fragmented or universal. (shrink)

This research project aims to accomplish two primary objectives: (1) propose an argument that a posthuman ethics in the design of technologies is sound and thus warranted and, (2) how can existent SBD approaches begin to envision principled and methodological ways of incorporating nonhuman values into design. In order to do this this MRP will provide a rudimentary outline of what constitutes SBD approaches. A particular design approach - Value Sensitive Design (VSD) - is taken up as an illustrative example (...) given that it, among the other SBD frameworks, most clearly illustrates a principled approach to the integration of values in design. -/- This explication will be followed by the strongest arguments for a posthumanist ethic, primarily drawing from the works of the Italian philosophers Leonardo Caffo and Roberto Marchesini and Francesa Ferrando. In doing so I will show how the human imperative to account for nonhuman values is a duty and as such must be continually ready-to-hand when making value-critical decision. (shrink)

Abstract. Death seems to be a permanent event, but there is no actual proof of its irreversibility. Here we list all known ways to resurrect the dead that do not contradict our current scientific understanding of the world. While no method is currently possible, many of those listed here may become feasible with future technological development, and it may even be possible to act now to increase their probability. The most well-known such approach to technological resurrection is cryonics. Another method (...) is indirect mind uploading, or digital immortality, namely the preservation of data about a person to allow for future reconstruction by powerful AI. More speculative ways to immortality include combinations of future superintelligence on a galactic scale, which could use simulation to resurrect all possible people, and new physical laws, which may include time-travel or obtaining information from the past. Acausal trade with parallel worlds could help combine random resurrection and reconstruction based on known data without loss of share of worlds where I exist (known as existence measure). Quantum immortality could help to increase the probability of success for cryonics and digital immortality. There many possible approaches to technological resurrection and thus if large-scale future technological development occurs, some form of resurrection is inevitable. (shrink)

Atomically precise manufacturing (APM) is the assembly of materials with atomic precision. APM does not currently exist, and may not be feasible, but if it is feasible, then the societal impacts could be dramatic. This paper assesses the net societal impacts of APM across the full range of important APM sectors: general material wealth, environmental issues, military affairs, surveillance, artificial intelligence, and space travel. Positive effects were found for material wealth, the environment, military affairs (specifically nuclear disarmament), and space travel. (...) Negative effects were found for military affairs (specifically rogue actor violence and AI. The net effect for surveillance was ambiguous. The effects for the environment, military affairs, and AI appear to be the largest, with the environment perhaps being the largest of these, suggesting that APM would be net beneficial to society. However, these factors are not well quantified and no definitive conclusion can be made. One conclusion that can be reached is that if APM R&D is pursued, it should go hand-in-hand with effective governance strategies to increase the benefits and reduce the harms. (shrink)

From his preliminary analysis in 1965, Hubert Dreyfus projected a future much different than those with which his contemporaries were practically concerned, tempering their optimism in realizing something like human intelligence through conventional methods. At that time, he advised that there was nothing “directly” to be done toward machines with human-like intelligence, and that practical research should aim at a symbiosis between human beings and computers with computers doing what they do best, processing discrete symbols in formally structured problem domains. (...) Fast-forward five decades, and his emphasis on the difference between two essential modes of processing, the unconscious yet purposeful mode fundamental to situated human cognition, and the “minded” sense of conscious processing characterizing symbolic reasoning that seems to lend itself to explicit programming, continues into the famous Dreyfus–McDowell debate. The present memorial reviews Dreyfus’ early projections, asking if the fears that punctuate current popular commentary on AI are warranted, and in light of these if he would deliver similar practical advice to researchers today. (shrink)