A wave of recent scholarship has warned about the potential for discriminatory harms of algorithmic systems, spurring an interest in algorithmic accountability and regulation. Meanwhile, parallel concerns about surveillance practices have already led to multiple successful regulatory efforts of surveillance technologies—many of which have algorithmic components. Here, we examine municipal surveillance regulation as offering lessons for algorithmic oversight. Taking the 2017 Seattle Surveillance Ordinance as our primary case study and surveying efforts across five other cities, we describe the features of (...) existing surveillance regulation; including procedures for describing surveillance technologies in detail, requirements for public engagement, and processes for establishing acceptable uses. Although the Seattle Surveillance Ordinance was not intended to address algorithmic accountability, we find these considerations to be relevant to the law’s aim of surfacing disparate impacts of systems in use. We also find that in notable cases government employees did not identify regulated algorithmic surveillance technologies as reliant on algorithmic or machine learning systems, highlighting definitional gaps that could hinder future efforts toward algorithmic regulation. We argue that finer-grained distinctions between types of information systems in the language of law and policy, and risk assessment tools integrated into their implementation would strengthen future regulatory efforts by rendering underlying algorithmic components more legible and accountable to political and community stakeholders. (shrink)

What is the class of possible semiotic systems? What kinds of systems could count as such systems? The human mind is naturally considered the prototypical semiotic system. During years of research in semiotics the class has been broadened to include i.e. living systems like animals, or even plants. It is suggested in the literature on artificial intelligence that artificial agents are typical examples of symbol-processing entities. It also seems that semiotic processes are in fact cognitive processes. In consequence, it is (...) natural to ask the question about the relation between semiotic studies and research on artificial cognitive systems within cognitive science. Consequently, my main question concerns the problem of inclusion or exclusion from the semiotic spectrum at least some artificial systems. I would like to consider some arguments against the possibility of artificial semiotic systems and I will try to repeal them. Then I will present an existing natural-language using agent of the SNePS system and interpret it in terms of Peircean theory of signs. I would like also to show that some properties of semiotic systems in Peircean sense could be also found in a discussed artificial system. Finally, I will have some remarks on the status of semiotics in general. (shrink)

The aim of this article is to strengthen links between cognitive brain research and formal logic. The work covers three fundamental sorts of logical inferences: reasoning in the propositional calculus, i.e. inferences with the conditional “if...then”, reasoning in the predicate calculus, i.e. inferences based on quantifiers such as “all”, “some”, “none”, and reasoning with n-place relations. Studies with brain-damaged patients and neuroimaging experiments indicate that such logical inferences are implemented in overlapping but different bilateral cortical networks, including parts of the (...) fronto-temporal cortex, the posterior parietal cortex, and the visual cortices. I argue that these findings show that we do not use a single deterministic strategy for solving logical reasoning problems. This account resolves many disputes about how humans reason logically and why we sometimes deviate from the norms of formal logic. (shrink)

The model proposed by Redish et al. considers vulnerabilities within decision systems based on expectancy-value assumptions. Further understanding of processes leading to addiction can be gained by considering other inputs to decision-making, particularly affective associations with behaviors. This consideration suggests additional decision-making vulnerabilities that might explain addictive behaviors.

The urgency of the paper is to explore the impact of the latest discoveries of the exact and human sciences on the activities of higher education in its ideological perspective. There is a tendency to increasing interest of medical students not only in professional issues, but also in the general atmosphere of changes in health care and the requirements already formulated by the society of the future. The purpose of the article is to continue our study of the historical and (...) philosophical evolution of humanistic ideas and views from the ideas of the times of Antiquity up to modern theories and futuristic predictions. The problem of the study is to analyze the phenomenon of transhumanism as the result of unrealistic expectations about the easy implementation of most people's dreams of health, happiness and even immortality. The prospects of development of means and methods of "artificial intelligence" are also studied. The research methods consist of combining the achievements of classical philosophy with the successes of the youngest sciences that study human beings, operate large data, use foresight and other means of forecasting. The central new notions are noosciences and nootechnologies, which are able to save mankind from the threats to its existence. Results of the research : a philosophical analysis of the new worldview – transhumanism has been undertaken in the article. The reasons for its appearance in the second half of the twentieth century are explained. The social and scientific sources and the main stages of the consolidation of supporters of transhumanism in the world and national associations have been studied. The variety of proposals of transhumanists and their relative unity as to the need for "human perfection" have been also studied. The main variants of "Transhumanistic manifestos " have been analyzed and showed the existence of two flows of proposals - technological and those oriented on changes in human behavior. It is proved that in our time transhumanism is an option of fundraising - the collection of donations from the very rich people who strive to live longer than it is possible now. It is indicated that transhumanism is supported by the male segment of the citizens of the West, who are keen on technological progress and, above all, fear of death. The position of T-m is positive for medical research. The conclusion s of our study showed the fact that existing variants of transhumanistic studies use only a minority of modern and promising technologies. Even leading scientists from Western Europe do not notice that among the nanotechnologies, the number of environmentally friendly nootechnologies is slowly growing. A group of scholars are studying these technologies and offering an increasing number of noosciences. We have chosen for our study the notion of "noohumanism". The article proves that in worldview and educational aspects noohumanism based on the noosciences has not only significant creative advantages over transhumanism, but is also devoid of its shortcomings. Medical and other sectors of higher education in Ukraine should rely on noohumanism, not transhumanism. (shrink)

Artificial Intelligence (AI) pervades humanity in 2022, and it is notoriously difficult to understand how certain aspects of it work. There is a movement—_Explainable_ Artificial Intelligence (XAI)—to develop new methods for explaining the behaviours of AI systems. We aim to highlight one important philosophical significance of XAI—it has a role to play in the elimination of vagueness. To show this, consider that the use of AI in what has been labeled _surveillance capitalism_ has resulted in humans quickly gaining the capability (...) to identify and classify most of the occasions in which languages are used. We show that the knowability of this information is incompatible with what a certain theory of vagueness—_epistemicism_—says about vagueness. We argue that one way the epistemicist could respond to this threat is to claim that this process brought about the end of vagueness. However, we suggest an alternative interpretation, namely that epistemicism is false, but there is a weaker doctrine we dub _technological epistemicism_, which is the view that vagueness is due to ignorance of linguistic usage, but the ignorance can be overcome. The idea is that knowing more of the relevant data and how to process it enables us to know the semantic values of our words and sentences with higher confidence and precision. Finally, we argue that humans are probably not going to believe what future AI algorithms tell us about the sharp boundaries of our vague words unless the AI involved can be explained in terms understandable by humans. That is, if people are going to accept that AI can tell them about the sharp boundaries of the meanings of their words, then it is going to have to be XAI. (shrink)

The focus of this paper are the ethical, legal and social challenges for ensuring the responsible use of “big brain data”—the recording, collection and analysis of individuals’ brain data on a large scale with clinical and consumer-directed neurotechnological devices. First, I highlight the benefits of big data and machine learning analytics in neuroscience for basic and translational research. Then, I describe some of the technological, social and psychological barriers for securing brain data from unwarranted access. In this context, I then (...) examine ways in which safeguards at the hardware and software level, as well as increasing “data literacy” in society, may enhance the security of neurotechnological devices and protect the privacy of personal brain data. Regarding ethical and legal ramifications of big brain data, I first discuss effects on the autonomy, the sense of agency and authenticity, as well as the self that may result from the interaction between users and intelligent, particularly closed-loop, neurotechnological devices. I then discuss the impact of the “datafication” in basic and clinical neuroscience research on the just distribution of resources and access to these transformative technologies. In the legal realm, I examine possible legal consequences that arises from the increasing abilities to decode brain states and their corresponding subjective phenomenological experiences on the hitherto inaccessible privacy of these information. Finally, I discuss the implications of big brain data for national and international regulatory policies and models of good data governance. (shrink)

Проблема осмислення машинного інтелекту постає для сучасної філософії як комплексне та багатовимірне завдання. Одним з найбільш дискусійних питань в цьому контексті є створення і гіпотетичне майбутнє машинного інтелекту, що самовизначається. Особливої актуальності у цьому зв’язку набувають також різні трансгуманістичні ідеї. Ці ідеї висловлюють турботу про певне благо, хоча й у своєму специфічному розумінні. Мається на увазі благо як для сучасної людини, так і для тих, кого розглядають у якості інших форм людини. В ході взаємозбагачення понять, що позначають різні напрямки комп’ютеризації (...) і трансгуманізму, виникає дещо якісно нове: поняття машинного інтелекту, що самовизначається, в його сучасному вигляді. Однак повний опис і адекватне розуміння такого типу інтелекту є надскладним завданням. В цьому випадку потрібно створення загальної «метамоделі», яка «обіцяє» досягнення мети. Така «метамодель» повинна послідовно породжувати відповідні дослідницькі проблеми для більш локальних досліджень. Але на певному етапі вона може виявитися однією з тих самих «парадигм», що, як писав Т. Кун, припинили породжувати дослідницькі проблеми й замість цього перетворилися на засоби інженерних дисциплін. Отже, потрібно відзначити, що заперечення важливості реагування на можливі загрози на рівні загальної «метамоделі» може надати прецедент для нереагування і на будь-яку іншу «менш гіпотетичну» небезпеку. Адекватним методологічним принципом для вирішення цієї проблеми, на нашу думку, є принцип обережності. Згідно з цим принципом наукове співтовариство і суспільство загалом повинні йти назустріч одне одному. Мова йде як про пізнання реального підґрунтя побоювань суспільства щодо невизначеності перспектив науково-технічного зростання, так і про популяризацію наукових знань, подолання міфів, пов’язаних з наукою та популярних серед неспеціалістів, тощо. Слід створювати резерви для відповідей на ймовірні, а не тільки на впевнено прогнозовані загрози. Ці резерви мають створюватися і в ході подальших наукових досліджень, і в ході гуманізації науки та суспільства, в ході гуманізації людської ментальності як глибинного, стійкого чинника духовної сфери. Отже, саме ці процеси забезпечують вже зараз і забезпечуватимуть у майбутньому готовність суспільства адекватно реагувати на кризові ситуації. (shrink)

In order to employ and exhibit our Simon-inspired approach to computational economics, and specifically defend our version of the view that even logically untrained humans are rational, albeit no more than “boundedly” so, we provide two models, both rooted in computational logic, of how it is that logically untrained humans perform in a seemingly irrational fashion in a particular “deviant” auction (the bi-pay auction).

An intelligent machine surpassing human intelligence across a wide set of skills has been proposed as a possible existential catastrophe. Among those concerned about existential risk related to artificial intelligence, it is common to assume that AI will not only be very intelligent, but also be a general agent. This article explores the characteristics of machine agency, and what it would mean for a machine to become a general agent. In particular, it does so by articulating some important differences between (...) belief and desire in the context of machine agency. One such difference is that while an agent can by itself acquire new beliefs through learning, desires need to be derived from preexisting desires or acquired with the help of an external influence. Such influence could be a human programmer or natural selection. We argue that to become a general agent, a machine needs productive desires, or desires that can direct behavior across multiple contexts. However, productive desires cannot sui generis be derived from non-productive desires. Thus, even though general agency in AI could in principle be created by human agents, general agency cannot be spontaneously produced by a non-general AI agent through an endogenous process. In conclusion, we argue that a common AI scenario, where general agency suddenly emerges in a non-general agent AI, such as DeepMind’s superintelligent board game AI AlphaZero, is not plausible. (shrink)

Scientists routinely solve the problem of supplementing one’s store of variables with new theoretical posits that can explain the previously inexplicable. The banality of success at this task obscures a remarkable fact. Generating hypotheses that contain novel variables and accurately project over a limited amount of additional data is so difficult—the space of possibilities so vast—that succeeding through guesswork is overwhelmingly unlikely despite a very large number of attempts. And yet scientists do generate hypotheses of this sort in very few (...) tries. I argue that this poses a dilemma: either the long history of scientific success is a miracle, or there exists at least one method or algorithm for generating novel hypotheses with at least limited projectibility on the basis of what’s available to the scientist at a time, namely a set of observations, the history of past conjectures, and some prior theoretical commitments. In other words, either ordinary scientific success is miraculous or there exists a logic of discovery at the heart of actual scientific method. (shrink)

When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. Making this suggestion both precise (...) and compelling is surprisingly difficult. Toward this end, I propose two fundamental rationales for randomization, drawing upon diverse ideas and results from the wider theory of computation. The first unifies common intuitions in favour of randomization from the aforementioned disciplines. The second introduces a deep connection between randomization and memory: access to a randomizing device is provably helpful for an agent burdened with a finite memory. Aside from fit with ordinary intuitions about rational action, the two rationales also make sense of empirical observations in the biological world. Indeed, random behaviour emerges more or less where it should, according to the proposal. (shrink)

Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically (...) identifying, organizing, and retrieving information, and for encoding episodic events by time and stimuli. Inspired by prominent human memory models, we propose associative networks as a preferred representation. Networks are ideal for their parsimony, flexibility, and ease of inspection. Networks also possess topological properties—such as clusters, hubs, and the small world—that aid analysis and navigation in an information space. Our cognitive perspective addresses fundamental problems faced by information management systems, in particular the retrieval of related items and the representation of context. We present evidence from neuroscience and memory research in support of this approach, and discuss the implications of systems design within the constraints of P-MAK’s principles, using text documents as an illustrative semantic domain. (shrink)

Future weapons will make life-or-death decisions without a human in the loop. When such weapons inflict unwarranted harm, no one appears to be responsible. There seems to be a responsibility gap. I first reconstruct the argument for such responsibility gaps to then argue that this argument is not sound. The argument assumes that commanders have no control over whether autonomous weapons inflict harm. I argue against this assumption. Although this investigation concerns a specific case of autonomous weapons systems, I take (...) steps towards vindicating the more general idea that superiors can be morally responsible in virtue of being in command. (shrink)

While art and science still functioned side-by-side during the Renaissance, their methods and perspectives diverged during the nineteenth century, creating a still enduring separation between the "two cultures". Recently, artists and scientists again collaborate more frequently, as promoted most radically by the ArtScience movement. This approach aims at a true synthesis between the intuitive, imaginative methods of art and the rational, rule-governed methods of science. To prepare the grounds for a theoretical synthesis, this paper surveys the fundamental commonalities and differences (...) between science and art. Science and art are united in their creative investigation, where coherence, pattern or meaning play a vital role in the development of concepts, while relying on concrete representations to experiment with the resulting insights. On the other hand, according to the standard conception, science seeks an understanding that is universal, objective and unambiguous, while art focuses on unique, subjective and open-ended experiences. Both offer prospect and coherence, mystery and complexity, albeit with science preferring the former and art, the latter. The paper concludes with some examples of artscience works that combine all these aspects. (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.

General Ontological Language (GOL) is a formal framework for representing and building ontologies. The purpose of GOL is to provide a system of top-level ontologies which can be used as a basis for building domain-specific ontologies. The present paper gives an overview about the basic categories of the GOL-ontology. GOL is part of the work of the research group Ontologies in Medicine (Onto-Med) at the University of Leipzig which is based on the collaborative work of the Institute of Medical Informatics (...) (IMISE) and the Institute for Computer Science (IfI). It represents work in progress toward a proposal for an integrated family of top-level ontologies and will be applied to several fields of medicine, in particular to the field of Clinical Trials. (shrink)

The paper presents an ethical analysis and constructive critique of the current practice of AI ethics. It identifies conceptual substantive and procedural challenges and it outlines strategies to address them. The strategies include countering the hype and understanding AI as ubiquitous infrastructure including neglected issues of ethics and justice such as structural background injustices into the scope of AI ethics and making the procedures and fora of AI ethics more inclusive and better informed with regard to philosophical ethics. These measures (...) integrate the perspective of AI justice into AI ethics, strengthening its capacity to provide comprehensive normative orientation and guidance for the development and use of AI that actually improves human lives and living together. (shrink)

In this paper, we use some elements of the philosophical theories of Wilfrid Sellars and Robert Brandom for examining the interactions between humans and machines. In particular, we adopt the concept of the space of reasons for analyzing the status of artificial intelligent agents. One could argue that AIAs, like the widely used recommendation systems, have already entered the space of reasons, since they seem to make knowledge claims that we use as premises for further claims. This, in turn, can (...) lead to a sense of alienation because AIAs do not quite play by the rules of the space of the reason. We, therefore, ask somewhat pointedly whether aliens have entered the space of reasons. A closer look reveals that it is a misconception to consider AIAs as being in the space of reasons. In fact, they should be seen as very sophisticated tools. Since these tools affect our own acting in the space of reasons, special regulations are required for their proper use. (shrink)

The possibility of interacting with remote services in natural language opens up new opportunities for sharing knowledge and for automating services. Easy-to-use, text-based interfaces might provide more democratic access to legal information, government services, and everyday knowledge as well. However, the methodology of engineering robust natural language interfaces is very diverse, and widely deployed solutions are still yet to come. The main contribution is a detailed problem analysis on the theoretical level, which reveals that a text-based interface is best understood (...) as an artificial agent that represents the interests of the remote party who is separated in time and space from the client. A possible ethical issue about the development of such an agent is also discussed. (shrink)

Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.

I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight the core technical and philosophical advantages of the learning approach for modeling moral cognition, and for addressing the so-called value (...) alignment problem in AI. (shrink)

An agent exercises instrumental rationality to the degree that she adopts appropriate means to achieving her ends. Adopting appropriate means to achieving one’s ends can, in turn, involve overcoming one’s strongest desires, that is, it can involve exercising synchronic self-control. However, contra prominent approaches, I deny that synchronic self-control is possible. Specifically, I draw on computational models and empirical evidence from cognitive neuroscience to describe a naturalistic, multi-system model of the mind. On this model, synchronic self-control is impossible. Must we, (...) then, give up on a meaningful conception of instrumental rationality? No. A multi-system view still permits something like synchronic self-control: an agent can control her very strong desires. Adopting a multi-system model of the mind thus places limitations on our conceptions of instrumental rationality, without requiring that we abandon the notion altogether. (shrink)

Our scientific theories, like our cognitive structures in general, consist of propositions linked by evidential, explanatory, probabilistic, and logical connections. Those theoretical webs ‘impinge on the world at their edges,’ subject to a continuing barrage of incoming evidence. Our credences in the various elements of those structures change in response to that continuing barrage of evidence, as do the perceived connections between them. Here we model scientific theories as Bayesian nets, with credences at nodes and conditional links between them modelled (...) as conditional probabilities. We update those networks, in terms of both credences at nodes and conditional probabilities at links, through a temporal barrage of random incoming evidence. Robust patterns of punctuated equilibrium, suggestive of ‘normal science’ alternating with ‘paradigm shifts,’ emerge prominently in that change dynamics. The suggestion is that at least some of the phenomena at the core of the Kuhnian tradition are predictable in the typical dynamics of scientific theory change captured as Bayesian nets under even a random evidence barrage. (shrink)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming (...) in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

The classical view of computing positions computation as a closed-box transformation of inputs (rational numbers or finite strings) to outputs. According to the interactive view of computing, computation is an ongoing interactive process rather than a function-based transformation of an input to an output. Specifically, communication with the outside world happens during the computation, not before or after it. This approach radically changes our understanding of what is computation and how it is modeled. The acceptance of interaction as a new (...) paradigm is hindered by the Strong Church–Turing Thesis (SCT), the widespread belief that Turing Machines (TMs) capture all computation, so models of computation more expressive than TMs are impossible. In this paper, we show that SCT reinterprets the original Church–Turing Thesis (CTT) in a way that Turing never intended; its commonly assumed equivalence to the original is a myth. We identify and analyze the historical reasons for the widespread belief in SCT. Only by accepting that it is false can we begin to adopt interaction as an alternative paradigm of computation. We present Persistent Turing Machines (PTMs), that extend TMs to capture sequential interaction. PTMs allow us to formulate the Sequential Interaction Thesis, going beyond the expressiveness of TMs and of the CTT. The paradigm shift to interaction provides an alternative understanding of the nature of computing that better reflects the services provided by today’s computing technology. (shrink)

We introduce a multimodal framework of deontic action logic which encodes the interaction between two fundamental procedures in normative reasoning: conceptual classification and deontic classification. The expressive power of the framework is noteworthy, since it combines insights from agency logic and dynamic logic, allowing for a representation of many kinds of normative conflicts. We provide a semantic characterization for three axiomatic systems of increasing strength, showing how our approach can be modularly extended in order to get different levels of analysis (...) of normative reasoning. Finally, we discuss ways in which the framework can be used to capture other formalisms proposed in the literature, as well as to model searching problems in Artificial Intelligence. (shrink)

In this paper, we claim that the disambiguation ofreferring expressions in discourse can be formulated in terms automatedreasoners can address. Specifically, we show that consistency,informativity and minimality are criteria which (i) can be implementedusing automated reasoning tools and (ii) can be used to disambiguatenoun-noun compounds, metonymy and definite descriptions.

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)

The invention of the computer has revolutionized science. With respect to finding the essential structures of life, for example, it has enabled scientists not only to investigate empirical examples, but also to create and study novel hypothetical variations by means of simulation: ‘life as it could be’. We argue that this kind of research in the field of artificial life, namely the specification, implementation and evaluation of artificial systems, is akin to Husserl’s method of free imaginative variation as applied to (...) the specific regional ontology of biology. Thus, at a time when the clarification of the essence of our biological embodiment is of growing interest for phenomenology, we suggest that artificial life should be seen as a method of externalizing some of the insurmountable complexity of imaginatively varying the phenomenon of life. (shrink)

Bayesian epistemologists support the norms of probabilism and conditionalization using Dutch book and accuracy arguments. These arguments assume that rationality requires agents to maximize practical or epistemic value in every doxastic state, which is evaluated from a subjective point of view (e.g., the agent’s expectancy of value). The accuracy arguments also presuppose that agents are opinionated. The goal of this paper is to discuss the assumptions of these arguments, including the measure of epistemic value. I have designed AI agents based (...) on the Bayesian model and a nonmonotonic framework and tested how they achieve practical and epistemic value in conditions in which an alternative set of assumptions holds. In one of the tested conditions, the nonmonotonic agent, which is not opinionated and fulfills neither probabilism nor conditionalization, outperforms the Bayesian in the measure of epistemic value that I argue for in the paper (α-value). I discuss the consequences of these results for the epistemology of rationality. (shrink)

This essay presents and reflects upon the construction of a few experimental artworks, among them Caracolomobile , that looks for poetic, aesthetic and functional possibilities to bring computer systems to the sensitive universe of human emotions, feelings and expressions. Modern and Contemporary Art have explored such qualities in unfathomable ways and nowadays is turning towards computer systems and their co-related technologies. This universe characterizes and is the focus of these experimental artworks; artworks dealing with entwined subjective and objective qualities, weaving (...) perceptions, sensations and concepts. One of them, Caracolomobile, features an art installation creating a set up for an artificial robot that recognizes humans’ affective states and answers them with movements and sounds. The robot was installed over an artificial mirror lake in an open indigo-blue space surrounded by mirrors. It perceives and discriminates human emotional states and expressions using an interface developed with a non-intrusive neural headset (The neural headset used was developed by Emotiv Systems: http://www.emotiv.com . Accessed 11 August 2011). This artwork raises questions and looks for answers inquiring about the preliminary steps for the creation of artefacts that would conduct one to poetically experiment with affect, emotion, sensations and feelings in computational systems. Other works in progress ask about the poetic possibilities of mixing computational autonomous processes and behavioural robotic procedures (Arkin 1998 ) to create artificial environments mixed with humans. (shrink)

As public administrations adopt artificial intelligence (AI), we see this transition has the potential to transform public service and public policies, by offering a rapid turnaround on decision making and service delivery. However, a recent series of criticisms have pointed to problematic aspects of mainstreaming AI systems in public administration, noting troubled outcomes in terms of justice and values. The argument supplied here is that any public administration adopting AI systems must consider and address ambiguities and uncertainties surrounding two key (...) dimensions: the algorithms’ results, and how public managers make decisions for and about AI systems’ design. The article is based on bibliographic research in institutional theory perspective that relates the design of AI systems and relevant literature on the decision-making process in public policy. The main finding is to explain why autonomous decision systems continue to reproduce ambiguities and uncertainties when applied in public administration and propose a reflection on AI governance in public administration. This article points to the need for design institutions that immerse themselves in understanding the nuances, details, and potential outcomes of AI governance for public administration. Such institutions would reconcile consequentialist logic with a logic of appropriateness to help navigate and mediate ambiguities and uncertainties. (shrink)

The relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, but (...) interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research. While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation. (shrink)

Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In (...) this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs. (shrink)

Rational decision making depends on what one believes, what one desires, and what one knows. In conventional decision models, beliefs are represented by probabilities and desires are represented by utilities. Software agents are knowledgeable entities capable of managing their own set of beliefs and desires, and they can decide upon the next operation to execute autonomously. They are also interactive entities capable of filtering communications and managing dialogues. Knowledgeability includes representing knowledge about the external world, reasoning with it, and sharing (...) it. Interactions include negotiations to perform tasks in cooperative, coordinative, and competitive ways. In this paper we focus on decision-making mechanisms for agent-based systems on the basis of agent interaction. We identify possible interaction scenarios and define mechanisms for decision making in uncertain environments. It is believed that software agents will become the underlying technology that offers the capability of distribution of competence, control, and information for the next generation of ubiquitous, distributed, and heterogeneous information systems. (shrink)

Many philosophers and psychologists now argue that emotions play a vital role in reasoning. This paper explores one particular way of elucidating how emotions help reason which may be dubbed ?the search hypothesis of emotion?. After outlining the search hypothesis of emotion and dispensing with a red herring that has marred previous statements of the hypothesis, I discuss two alternative readings of the search hypothesis. It is argued that the search hypothesis must be construed as an account of what emotions (...) typically do, rather than as a definition of emotion. Even as an account of what emotions typically do, the search hypothesis can only be evaluated in the context of a specific theory of what emotions are. 1 Introduction 2 The search hypothesis of emotion 3 A red herring: the frame problem 4 The search problem 5 Two readings of the search hypothesis 6 Two final remarks 7 Conclusion. (shrink)

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences.

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)

I use a theorem from machine learning, called the “No Free Lunch” theorem to support the claim that non-epistemic values are essential to theory choice. I argue that NFL entails that predictive accuracy is insufficient to favor a given theory over others, and that NFL challenges our ability to give a purely epistemic justification for using other traditional epistemic virtues in theory choice. In addition, I argue that the natural way to overcome NFL’s challenge is to use non-epistemic values. If (...) my argument holds, non-epistemic values are entangled in theory choice regardless of human limitations and regardless of the subject matter. Thereby, my argument overcomes objections to the main lines of argument revealing the role of values in theory choice. At the end of the paper, I argue that, contrary to common conception, the epistemic challenge arising from NFL is distinct from Hume’s problem of induction and other forms of underdetermination. (shrink)

We describe how Intuitionistic Linear Logic can be used to provide a unified logical account for agents to find and execute plans. This account supports the modelling of agent interaction, including dialogue; allows agents to be robust to unexpected events and failures; and supports significant reuse of agent specifications. The framework has been implemented and several case studies have been considered. Further applications include human–computer interfaces as well as agent interaction in the semantic web.

We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group’s expertise.

Agency is a central concept in the organisational approach to organisms, which accounts for their internal purposiveness. Recent recognition of the active role played by organisms in evolution has led researchers to use this concept in an evolutionary approach. Agency is then considered in terms of ‘unintentional’ choice: agents choose from a given repertoire the behaviour most appropriate to their goal, with this choice influencing evolutionary pathways. This view, while allowing for the evolutionary role of the activity of organisms, presents (...) two pitfalls. First, it restricts organisms’ agency by confining their choice within the bounds of a behavioural repertoire, and assuming their goals are dictated by natural selection. Second, this view, while claiming to eliminate the idea of intentionality, retains its structure: organisms are portrayed as rational entities, persistently pursuing specific goals. This leads us back to a teleological thinking, whose use in evolutionary theory has already been heavily criticised. This paper proposes a conception of biological agency which does not assume goal-directedness but considers agency as inventiveness. An organism will be said to be an agent if it is the triggering cause of behaviours falling outside the known repertoire and whose form can only be explained by the unique relationship between the organism and the environment. If these behaviours have implications in evolution, the agent will be considered an evolutionary agent. The merit of this approach is further validated by evidencing the significant role behavioural innovations play in evolution. Finally, the last section delves into the process of invention by examining animal play. (shrink)

The impacts that AI and robotics systems can and will have on our everyday lives are already making themselves manifest. However, there is a lack of research on the ethical impacts and means for amelioration regarding AI and robotics within tourism and hospitality. Given the importance of designing technologies that cross national boundaries, and given that the tourism and hospitality industry is fundamentally predicated on multicultural interactions, this is an area of research and application that requires particular attention. Specifically, tourism (...) and hospitality have a range of context-unique stakeholders that need to be accounted for in the salient design of AI systems is to be achieved. This paper adopts a stakeholder approach to develop the conceptual framework to centralize human values in designing and deploying AI and robotics systems in tourism and hospitality. The conceptual framework includes several layers – ‘Human-human-AI’ interaction level, direct and indirect stakeholders, and the macroenvironment. The ethical issues on each layer are outlined as well as some possible solutions to them. Additionally, the paper develops a research agenda on the topic. (shrink)

Autonomy is often considered a core value of Western society that is deeply entrenched in moral, legal, and political practices. The development and deployment of artificial intelligence (AI) systems to perform a wide variety of tasks has raised new questions about how AI may affect human autonomy. Numerous guidelines on the responsible development of AI now emphasise the need for human autonomy to be protected. In some cases, this need is linked to the emergence of increasingly ‘autonomous’ AI systems that (...) can perform tasks without human control or supervision. Do such ‘autonomous’ systems pose a risk to our own human autonomy? In this article, I address the question of a trade-off between human autonomy and system ‘autonomy’. (shrink)