Vincent C. Müller

In this essay, we suggest that there is a tendency towards total surveillance – and we are all to blame because we want privacy for ourselves, but surveillance for the others. Typically, we “the good people” are for privacy, and against surveillance; against the blanket surveillance of “security” agencies and the universal data collection of for-profit companies. But that position is complacent in the light of developments: Surveillance is not just top-down anymore. The increased data production, data access and data analysis mean that surveillance has become a universal phenomenon of everybody watching everybody else in the global village. We may want privacy, but we have supplemented surveillance with sousveillance and horizontal surveillance, and what we are getting is total surveillance of everyone by everyone.

Protecting life and restoring health have been at the core of the conception of medical care since antiquity, for health professionals and the public alike. Assisted dying represents a practice that appears to challenge this traditional conception. In jurisdictions where assisted dying is condoned, the question remains as to whether the practice should be medicalized or deferred to non-medical actors. Irrespective of the divisive controversy surrounding assisted dying and its medicalization per se, the practice gives rise to high-stakes ethical, legal and organizational challenges when intersecting with organ transplantation, tissue banking, and Structural Brain Preservation or cryonics. In this narrative account, we describe the contextual framework and outline our experience and operational scope for addressing them at a university hospital in Germany. Based on this description we propose a structured pathway for increasing donation and preservation options for terminal patients while retaining ethical integrity and professional accountability.

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.

There seems to be dilemma whether we should direct our efforts in AI ethics towards problems that are clearly visible on the horizon today (short-term), or towards problems for which we see significant probability of them occurring at some point (long-term), provided they are significant enough. Some authors have argued that we should put a heavy focus on the one or the other. I will argue that this is a false dilemma: Any rational agent will consider both short- and long-term consequences (as well as other factors). My analysis is that the supposed dilemma rests on the assumption that we are at a very unusual situation that forces favouring of one option – what I call an “ethical singularity”. The only serious argument for this view is that the longer term involves a) an ethically “significantly different” situation, and b) that this demands a version of “ethical fanaticism”. Both premises turn out to have little in their favour. We should thus return to the “normal balance” of expected utility in AI ethics and solve problems in all relevant time horizons concurrently.

It is known that big data analytics and AI pose a threat to privacy, and that some of this is due to some kind of “black box problem” in AI. I explain how this becomes a problem in the context of justification for judgments and actions. Furthermore, I suggest distinguishing three kinds of opacity: 1) the subjects do not know what the system does (“shallow opacity”), 2) the analysts do not know what the system does (“standard black box opacity”), or 3) the analysts cannot possibly know what the system might do (“deep opacity”). If the agents, data subjects as well as analytics experts, operate under opacity, then these agents cannot provide justifications for judgments that are necessary to protect privacy, e.g., they cannot give “informed consent”, or guarantee “anonymity.” It follows from these points that agents in big data analytics and AI ofen cannot make the judgments needed to protect privacy. So I conclude that big data analytics makes the privacy problems worse and the remedies less effective. As a positive note, I provide a brief outlook on technical ways to handle this situation.

Digital ethics, also known as computer ethics or information ethics, is now a lively field that draws a lot of attention, but how did it come about and what were the developments that lead to its existence? What are the traditions, the concerns, the technological and social developments that pushed digital ethics? How did ethical issues change with digitalisation of human life? How did the traditional discipline of philosophy respond? The article provides an overview, proposing historical epochs: ‘pre-modernity’ prior to digital computation over data, via the ‘modernity’ of digital data processing to our present ‘post-modernity’ when not only the data is digital, but our lives themselves are largely digital. In each section, the situation in technology and society is sketched, and then the developments in digital ethics are explained. Finally, a brief outlook is provided.

What proper role should considerations of risk, particularly to research subjects, play when it comes to conducting research on human enhancement in the military context? We introduce the currently visible military enhancement techniques (1) and the standard discussion of risk for these (2), in particular what we refer to as the ‘Assumption’, which states that the demands for risk-avoidance are higher for enhancement than for therapy. We challenge the Assumption through the introduction of three categories of enhancements (3): therapeutic, preventive, and pure enhancements. This demands a revision of the Assumption (4), alongside which we propose some further general principles bearing on how to balance risks and benefits in the context of military enhancement research. We identify a particular type of therapeutic enhancements as providing a more responsible path to human trials of the relevant interventions than pure enhancement applications. Finally, we discuss some possible objections to our line of thought (5). While acknowledging their potential insights, we ultimately find them to be unpersuasive, at least provided that our proposal is understood as fully non-coercive towards the candidates for such therapeutic enhancement trials.

Engineers fine-tune the design of robot bodies for control purposes, however, a methodology or set of tools is largely absent, and optimization of morphology (shape, material properties of robot bodies, etc.) is lagging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or ”soft” bodies. These carry substantial potential regarding their exploitation for control—sometimes referred to as ”morphological computation”. In this article, we briefly review different notions of computation by physical systems and propose the dynamical systems framework as the most useful in the context of describing and eventually designing the interactions of controllers and bodies. Then, we look at the pros and cons of simple vs. complex bodies, critically reviewing the attractive notion of ”soft” bodies automatically taking over control tasks. We address another key dimension of the design space—whether model-based control should be used and to what extent it is feasible to develop faithful models for different morphologies.

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of artificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understanding of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so we suggest that “AI Philosophy” provides a new method for philosophy.

Die Welt ist voller Leid. Gott ist entweder unfähig, es zu verhindern – dann ist Er nicht allmächtig –, oder Er will es nicht verhindern – dann ist Er nicht vollkommen gut. Seit Generationen wird dies als das schlagendste Argument gegen den Glauben angesehen, daß ein allmächtiges und allgütiges Wesen existiert. Natürlich haben Theisten sich die größte Mühe gegeben, eine angemessene Erwiderung vorzubringen. ... Selbst wenn nur ein einziges Individuum unnötigerweise für einen kurzen Moment eine leichte Unannehmlichkeit zu ertragen hätte, wäre das Problem logisch ebenso real – obwohl es nicht so schmerzhaft wäre oder möglicherweise sogar unbemerkt bliebe.

Tailoring the design of robot bodies for control purposes is implicitly performed by engineers, however, a methodology or set of tools is largely absent and optimization of morphology (shape, material properties of robot bodies, etc.) is lag- ging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or "soft" bodies. These carry substantial potential regarding their exploitation for control – sometimes referred to as "mor- phological computation" in the sense of offloading computation needed for control to the body. Here, we will argue in favor of a dynamical systems rather than com- putational perspective on the problem. Then, we will look at the pros and cons of simple vs. complex bodies, critically reviewing the attractive notion of “soft” bodies automatically taking over control tasks. We will address another key dimension of the design space – whether model-based control should be used and to what extent it is feasible to develop faithful models for different morphologies. ----- This paper was also published in the 2014 AISB proceedings http://aisb50.org/representation-of-reality-humans-animals-and-machines/ - http://doc.gold.ac.uk/aisb50/

Epistemic theories of truth, such as those presumed to be typical for anti-realism, can be characterised as saying that what is true can be known in principle: p → ◊Kp. However, with statements of the form “p & ¬Kp”, a contradiction arises if they are both true and known. Analysis of the nature of the paradox shows that such statements refute epistemic theories of truth only if the the anti-realist motivation for epistemic theories of truth is not taken into account. The motivation in a link of understandability ans meaningful- ness suggests to change the above principle and to restrict the theory to logically simple sentences, in which case the paradox does not arise. This suggestion also allows to see the deep philosophical problems for anti-realism those counterexamples are pointing at.

This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in more detail. We conclude that while there are very significant technical hurdles to real human enhancement through AI, and significant ethical problems, there are also significant benefits that may realistically be achieved in ways that are consonant with a rights-based ethics as well. We also highlight the specific concerns that apply particularly to applications of AI for "sheer" IA (more realistic in the near term), and to enhancement applications, respectively.

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of ar-tificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understand-ing of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so we suggest that “AI Philosophy” provides a new method for philosophy.

This book gathers contributions from the fourth edition of the Conference on "Philosophy and Theory of Artificial Intelligence" (PT-AI), held on 27-28th of September 2021 at Chalmers University of Technology, in Gothenburg, Sweden. It covers topics at the interface between philosophy, cognitive science, ethics and computing. It discusses advanced theories fostering the understanding of human cognition, human autonomy, dignity and morality, and the development of corresponding artificial cognitive structures, analyzing important aspects of the relationship between humans and AI systems, including the ethics of AI. This book offers a thought-provoking snapshot of what is currently going on, and what are the main challenges, in the multidisciplinary field of the philosophy of artificial intelligence.

The standard argument to the conclusion that artificial intelligence (AI) constitutes an existential risk for the human species uses two premises: (1) AI may reach superintelligent levels, at which point we humans lose control (the ‘singularity claim’); (2) Any level of intelligence can go along with any goal (the ‘orthogonality thesis’). We find that the singularity claim requires a notion of ‘general intelligence’, while the orthogonality thesis requires a notion of ‘instrumental intelligence’. If this interpretation is correct, they cannot be joined as premises and the argument for the existential risk of AI turns out invalid. If the interpretation is incorrect and both premises use the same notion of intelligence, then at least one of the premises is false and the orthogonality thesis remains itself orthogonal to the argument to existential risk from AI. In either case, the standard argument for existential risk from AI is not sound.—Having said that, there remains a risk of instrumental AI to cause very significant damage if designed or used badly, though this is not due to superintelligence or a singularity.

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.

In this review, we present some ethical imperatives observed in this pandemic from a data ethics perspective. Our exposition connects recurrent ethical problems in the discipline, such as, privacy, surveillance, transparency, accountability, and trust, to broader societal concerns about equality, discrimination, and justice. We acknowledge data ethics role as significant to develop technological, inclusive, and pluralist societies. - - - Resumen: En esta revisión, exponemos algunos de los imperativos éticos observados desde la ética de datos en esta pandemia. Nuestra exposición busca conectar problemas éticos típicos dentro de esta disciplina, a saber, privacidad, vigilancia, transparencia, responsabilidad y confianza, con preocupaciones a nivel social relacionadas con la igualdad, discrimi nación y justicia. Consideramos que la ética de datos tiene un rol significativo para desarrollar sociedades tecnificadas, inclusivas y pluralistas.

There are unusual challenges in ethics for RAS. Perhaps the issue can best be summarised as needing to consider “technically informed ethics”. The technology of RAS raises issues that have an ethical dimension, and perhaps uniquely so due to the possibility of moving human decision-making which is implicitly ethically informed to computer systems. Further, if seeking solutions to these problems – ethically aligned design, to use the IEEE’s terminology – then the solutions must be technically meaningful, capable of realisation, capable of assurance, and suitable as a basis for regulation. Thus, ethics for RAS is a rich, complex multi-disciplinary concern, and perhaps more complex than many other ethical issues facing society today. It is also fast-moving. This paper has endeavoured to give an accessible introduction to some of the key issues, noting that many of them are quite subtle, and it is not possible to do them full justice in such a short document. However, we have sought to counterbalance this by giving an extensive list of initiatives, standards, etc. that focus on ethics of RAS and AI, see Annex A.

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.

Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here.

The «extended mind thesis» sounds like a substantive thesis, the truth of which we should investigate. But actually the thesis a) turns about to be just a statement on where the demarcations for the «mental» are to be set (internal, external,…), i.e. it is about the «mark of the mental»; and b) the choice about the mark of the mental is a verbal choice, not a matter of scientific discovery. So, the «extended mind thesis » is a remark on how its supporters or opponents want to use the word ‘mind’, not a thesis of cognitive science or philosophy. The upshot of the extended mind discussion should not be to draw the line further out, but to drop the demarcation project.