There are a wide variety of potential applications of artificial intelligence (AI) in Defence settings, ranging from the use of autonomous drones to logistical support. However, limited research exists exploring how the public view these, especially in view of the value of public attitudes for influencing policy-making. An accurate understanding of the public’s perceptions is essential for crafting informed policy, developing responsible governance, and building responsive assurance relating to the development and use of AI in military settings. This study (...) is the first to explore public perceptions of and attitudes towards AI in Defence. A series of four focus groups were conducted with 20 members of the UK public, aged between 18 and 70, to explore their perceptions and attitudes towards AI use in general contexts and, more specifically, applications of AI in Defence settings. Thematic analysis revealed four themes and eleven sub-themes, spanning the role of humans in the system, the ethics of AI use in Defence, trust in AI versus trust in the organisation, and gathering information about AI in Defence. Participants demonstrated a variety of misconceptions about the applications of AI in Defence, with many assuming that a variety of different technologies involving AI are already being used. This highlighted a confluence between information from reputable sources combined with narratives from the mass media and conspiracy theories. The study demonstrates gaps in knowledge and misunderstandings that need to be addressed, and offers practical insights for keeping the public reliably, accurately, and adequately informed about the capabilities, limitations, benefits, and risks of AI in Defence. (shrink)

Perception is a first-person internal sensation induced within the nervous system at the time of arrival of sensory stimuli from objects in the environment. Lack of access to the first-person properties has limited viewing perception as an emergent property and it is currently being studied using third-person observed findings from various levels. One feasible approach to understand its mechanism is to build a hypothesis for the specific conditions and required circuit features of the nodal points where the mechanistic (...) operation of perception take place for one type of sensation in one species and to verify it for the presence of comparable circuit properties for perceiving a different sensation in a different species. The present work explains visual perception in mammalian nervous system from a first-person frame of reference and provides explanations for the homogeneity of perception of visual stimuli above flicker fusion frequency, the perception of objects at locations different from their actual position, the smooth pursuit and saccadic eye movements, the perception of object borders, and perception of pressure phosphenes. Using results from temporal resolution studies and the known details of visual cortical circuitry, explanations are provided for (a) the perception of rapidly changing visual stimuli, (b) how the perception of objects occurs in the correct orientation even though, according to the third-person view, activity from the visual stimulus reaches the cortices in an inverted manner and (c) the functional significance of well-conserved columnar organization of the visual cortex. A comparable circuitry detected in a different nervous system in a remote species-the olfactory circuitry of the fruit fly Drosophila melanogaster-provides an opportunity to explore circuit functions using genetic manipulations, which, along with high-resolution microscopic techniques and lipid membrane interaction studies, will be able to verify the structure-function details of the presented mechanism of perception. (shrink)

Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of (...) AI is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research. (shrink)

BackgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.MethodsSeventeen semi-structured interviews were conducted with birth (...) parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.ResultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.ConclusionsThe ethical values that underscore birth parents and mothers’ perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI. (shrink)

Dialogue systems, from Virtual Personal Assistants such as Siri, Cortana, and Alexa to state-of-the-art systems such as BlenderBot3 and ChatGPT, are already widely available, used in a variety of applications, and are increasingly part of many people’s lives. However, the task of enabling them to use empathetic language more convincingly is still an emerging research topic. Such systems generally make use of complex neural networks to learn the patterns of typical human language use, and the interactions in (...) which the systems participate are usually mediated either via interactive text-based or speech-based interfaces. In human–human interaction, empathy has been shown to promote prosocial behaviour and improve interaction. In the context of dialogue systems, to advance the understanding of how perceptions of empathy affect interactions, it is necessary to bring greater clarity to how empathy is measured and assessed. Assessing the way dialogue systems create perceptions of empathy brings together a range of technological, psychological, and ethical considerations that merit greater scrutiny than they have received so far. However, there is currently no widely accepted evaluation method for determining the degree of empathy that any given system possesses (or, at least, appears to possess). Currently, different research teams use a variety of automated metrics, alongside different forms of subjective human assessment such as questionnaires, self-assessment measures and narrative engagement scales. This diversity of evaluation practice means that, given two DSs, it is usually impossible to determine which of them conveys the greater degree of empathy in its dialogic exchanges with human users. Acknowledging this problem, the present article provides an overview of how empathy is measured in human–human interactions and considers some of the ways it is currently measured in human–DS interactions. Finally, it introduces a novel third-person analytical framework, called the Empathy Scale for Human–Computer Communication (ESHCC), to support greater uniformity in how perceived empathy is measured during interactions with state-of-the-art DSs. (shrink)

Following the studies of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018), this empirical study uses two scenario-based online experiments. The sample consists of 221 subjects from Germany, differing in both age and gender. The original studies are not replicated one-to-one. New scenarios are constructed as realistically as possible and focused on everyday work situations. They are based on the AI acceptance model of Scheuer (Grundlagen intelligenter KI-Assistenten und deren vertrauensvolle Nutzung. Springer, Wiesbaden, 2020) (...) and are extended by individual descriptive elements of AI systems in comparison to the original studies. The first online experiment examines decisions made by artificial intelligence with varying degrees of impact. In the high-impact scenario, applicants are automatically selected for a job and immediately received an employment contract. In the low-impact scenario, three applicants are automatically invited for another interview. In addition, the relationship between age and risk perception is investigated. The second online experiment tests subjects’ perceived trust in decisions made by artificial intelligence, either semi-automatically through the assistance of human experts or fully automatically in comparison. Two task types are distinguished. The task type that requires “human skills”—represented as a performance evaluation situation—and the task type that requires “mechanical skills”—represented as a work distribution situation. In addition, the extent of negative emotions in automated decisions is investigated. The results are related to the findings of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018). Implications for further research activities and practical relevance are discussed. (shrink)

Purpose The purpose of this study is to examine public perceptions and attitudes toward using artificial intelligence (AI) in the US criminal justice system. Design/methodology/approach The authors took a quantitative approach and administered an online survey using the Amazon Mechanical Turk platform. The instrument was developed by integrating prior literature to create multiple scales for measuring public perceptions and attitudes. Findings The findings suggest that despite the various attempts, there are still significant perceptions of sociodemographic bias in the criminal (...) justice system and technology alone cannot alleviate them. However, AI can assist judges in making fairer and more objective decisions by using triangulation – offering additional data points to offset individual biases. Social implications Other scholars can build upon the findings and extend the work to shed more light on some problems of growing concern for society – bias and inequality in criminal sentencing. AI can be a valuable tool to assist judges in the decision-making process by offering diverse viewpoints. Furthermore, the authors bridge the gap between the fields of technology and criminal justice and demonstrate how the two can be successfully integrated for the benefit of society. Originality/value To the best of the authors’ knowledge, this is among the first studies to examine a complex societal problem like the introduction of technology in a high-stakes environment – the US criminal justice system. Understanding how AI is perceived by society is necessary to develop more transparent and unbiased algorithms for assisting judges in making fair and equitable sentencing decisions. In addition, the authors developed and validated a new scale that can be used to further examine this novel approach to criminal sentencing in the future. (shrink)

Despite the immense societal importance of ethically designing artificial intelligence, little research on the public perceptions of ethical artificial intelligence principles exists. This becomes even more striking when considering that ethical artificial intelligence development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even (...) impossible, as developers must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case—the use of artificial intelligence in tax fraud detection. The results of a large conjoint survey suggest that, by and large, German respondents evaluate the ethical principles as equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred ethical principles but also in the importance levels of the principles themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on artificial intelligence. Societal implications, as well as design challenges, are discussed. (shrink)

Thanks to advances in machine learning in recent years the ability of AI agents to act independently of human oversight, respond to their environment, and interact with other machines has significantly increased, and is one step closer to human-like performance. For this reason, we can observe contemporary researchers’ efforts towards modeling agency in artificial systems. In this light, the aim of this paper is to develop a dual-process approach to the problem of AI agency perception, and to (...) discuss possible triggers of various agency perceptions. The article discusses the agency attribution phenomenon, based on which the argument for the dual-process nature of agency perception is developed. Two distinct types of thinking involved in human reasoning on AI agency are suggested: Type 1 and Type 2. The first one is fast, automatic, routine, and often unconscious; the second is a slower, controlled, more conscious one. These two distinct types of processing can yield differing and sometimes conflicting results for human cognition and interaction. The preliminary philosophical findings may contribute to further investigations in philosophy of mind or cognitive psychology and could also be empirically tested in HCI and UX studies. (shrink)

Thanks to advances in machine learning in recent years the ability of AI agents to act independently of human oversight, respond to their environment, and interact with other machines has significantly increased, and is one step closer to human-like performance. For this reason, we can observe contemporary researchers’ efforts towards modeling agency in artificial systems. In this light, the aim of this paper is to develop a dual-process approach to the problem of AI agency perception, and to (...) discuss possible triggers of various agency perceptions. The article discusses the agency attribution phenomenon, based on which the argument for the dual-process nature of agency perception is developed. Two distinct types of thinking involved in human reasoning on AI agency are suggested: Type 1 and Type 2. The first one is fast, automatic, routine, and often unconscious; the second is a slower, controlled, more conscious one. These two distinct types of processing can yield differing and sometimes conflicting results for human cognition and interaction. The preliminary philosophical findings may contribute to further investigations in philosophy of mind or cognitive psychology and could also be empirically tested in HCI and UX studies. (shrink)

The greatest rhetorical challenge to developers of creative artificial intelligence systems is convincingly arguing that their software is more than just an extension of their own creativity. This paper suggests that “creative autonomy,” which exists when a system not only evaluates creations on its own, but also changes its standards without explicit direction, is a necessary condition for making this argument. Rather than requiring that the system be hermetically sealed to avoid perceptions of human influence, developing creative autonomy (...) is argued to be more plausible if the system is intimately embedded in a broader society of other creators and critics. Ideas are presented for constructing systems that might be able to achieve creative autonomy. (shrink)

In this study, we analyse the public perception of military AI in Estonia, a techno-optimistic country with high support for science and technology. This study involved quantitative survey data from 2021 on the public’s attitudes towards AI-based technology in general, and AI in developing and using weaponised unmanned ground systems (UGS) in particular. UGS are a technology that has been tested in militaries in recent years with the expectation of increasing effectiveness and saving manpower in dangerous military tasks. (...) However, developing weaponised systems often raises questions about their safety and accountability. While there have been public debates about the possible outcomes of AI-based civilian technologies like self-driving cars, equivalent discussions have not been held about military technologies. A discourse on potentially sensitive technologies can diminish biased representations and the distribution of worst-case scenarios. The results of this study reveal that an overall positive attitude towards AI-driven technologies is related to a positive perception of military AI systems. However, trustworthiness and human control are the main aspects that are expected from military technologies. (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)

What is the relationship between perception and action, between an organism and its environment, in explaining consciousness? These are issues at the heart of philosophy of mind and the cognitive sciences. This book explores the relationship between perception and action from a variety of interdisciplinary perspectives, ranging from theoretical discussion of concepts to findings from recent scientific studies. It incorporates contributions from leading philosophers, psychologists, neuroscientists, and an artificial intelligence theorist. The contributions take a range of positions (...) with respect to the view that perception is an achievement by an agent acting in a complex environment in which sensorimotor dynamics constitute an essential ingredient to perceptual experience. A key focus of the book is on the debate about action-oriented theories of visual perception versus the dual-visual systems hypothesis The former champions the role of sensorimotor dynamics in perceptual awareness while the latter favours a functional dichotomy between perception and action. At least on the surface, these two approaches are in conflict. Where one emphasizes the interdependence of action and perception, the other suggests that action and perception are functionally distinct. The dialogue between these two approaches brings out wider theoretical issues underlying the research paradigm of cognitive sciences and philosophy of mind. Exploring one of the major debates in the philosophy and psychology, this book is fascinating reading for all those in the cognitive sciences and philosophy of mind. (shrink)

The detection of a human’s intended behavior is one of the most important skills that a social robot should have in order to become acceptable as a part of human society, because humans are used to understand the actions of other humans in a goal-directed manner and they will expect the social robot to behave similarly. A breakthrough in this area can advance several research branches related to social intelligence such as learning by imitation and mutual adaptation. To achieve this (...) goal the robot needs to integrate all possible evidence of intention and neglect the unintended behavior, and a complete solution should use low-level signal processing and high-level reasoning. This work explores the low-level signal processing part of the solution by proposing an interactive adaptive perception scheme that uses four important features of human behavior to amplify the signals originating from intended behavior with respect to signals originating from unintended behavior and other noise sources such as instrumental noise. This work follows the vision that intelligence is not only a function of a centralized sophisticated artificial brain, but can be presented in different forms in the entire robot including its perception and motion systems (the mind is not contained in the brain, but distributed in every cell in the body). A simple example of using the proposed scheme was implemented and the results of two experiments with it are also presented. (shrink)

The current research aims to answer the following question: “who will be held responsible for harm involving an artificial intelligence system?” Drawing upon the literature on moral judgments, we assert that when people perceive an AI system’s action as causing harm to others, they will assign blame to different entity groups involved in an AI’s life cycle, including the company, the developer team, and even the AI system itself, especially when such harm is perceived to be intentional. Drawing upon (...) the theory of mind perception, we hypothesized that two dimensions of mind: perceived agency—attributing intention, reasoning, pursuing goals, and communicating to AI, and perceived experience—attributing emotional states, such as feeling pain and pleasure, personality, and consciousness to AI—mediated the relationship between perceived intentional harm and blame judgments toward AI. We also predicted that people are likely to attribute higher mind characteristics to AI when harm is perceived to be directed to humans than when it is perceived to be directed to non-humans. We tested our research model in three experiments. In all experiments, we found that perceived intentional harm led to blame judgments toward AI. In two experiments, we found perceived experience, not agency, mediated the relationship between perceived intentional harm and blame judgments. We also found that companies and developers were held responsible for moral violations involving AI, with developers received the most blame among the entities involved. Our third experiment reconciles the findings by showing that perceived intentional harm directed to a non-human entity did not lead to increased attributions of mind to AI. These findings have implications for theory and practice concerning unethical outcomes and behavior associated with AI use. (shrink)

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

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association (...) areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

The increasing use of algorithms in allocating resources and services in both private industry and public administration has sparked discussions about their consequences for inequality and fairness in contemporary societies. Previous research has shown that the use of automated decision-making (ADM) tools in high-stakes scenarios like the legal justice system might lead to adverse societal outcomes, such as systematic discrimination. Scholars have since proposed a variety of metrics to counteract and mitigate biases in ADM processes. While these metrics focus on (...) technical fairness notions, they do not consider how members of the public, as most affected subjects by algorithmic decisions, perceive fairness in ADM. To shed light on subjective fairness perceptions of individuals, this study analyzes individuals’ answers to open-ended fairness questions about hypothetical ADM scenarios that were embedded in the German Internet Panel (Wave 54, July 2021), a probability-based longitudinal online survey. Respondents evaluated the fairness of vignettes describing the use of ADM tools across different contexts. Subsequently, they explained their fairness evaluation providing a textual answer. Using qualitative content analysis, we inductively coded those answers (_N_ = 3697). Based on their individual understanding of fairness, respondents addressed a wide range of aspects related to fairness in ADM which is reflected in the 23 codes we identified. We subsumed those codes under four overarching themes: _Human elements in decision-making_, _Shortcomings of the data_, _Social impact of AI_, and _Properties of AI_. Our codes and themes provide a valuable resource for understanding which factors influence public fairness perceptions about ADM. (shrink)

Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and whether they experience respectful treatment (i.e., interactional justice). In this experimental survey study with open-ended qualitative questions, we examine decision making in six HRM functions and manipulate the decision maker (AI or human) and decision valence (positive or negative) to determine their impact on individuals’ experiences of interactional justice, trust, dehumanization, and perceptions of decision-maker role (...) appropriate- ness. In terms of decision makers, the use of human decision makers over AIs generally resulted in better perceptions of respectful treatment. In terms of decision valence, people experiencing positive over negative decisions generally resulted in better perceptions of respectful treatment. In instances where these cases conflict, on some indicators people preferred positive AI decisions over negative human decisions. Qualitative responses show how people identify justice concerns with both AI and human decision making. We outline implications for theory, practice, and future research. (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)

The development of increasingly intelligent and autonomous technologies will eventually lead to these systems having to face morally problematic situations. This gave rise to the development of artificial morality, an emerging field in artificial intelligence which explores whether and how artificial systems can be furnished with moral capacities. This will have a deep impact on our lives. Yet, the methodological foundations of artificial morality are still sketchy and often far off from possible applications. One (...) important area of application of artificial systems with moral capacities is geriatric care. The goal of this article is to afford the methodological foundations for artificial morality, i.e., for implementing moral capacities in artificial systems in general, and to discuss them with respect to an assistive system in geriatric care which is capable of moral learning. (shrink)

In 2021, 193 Member States at UNESCO’s General Conference adopted the Recommendation on the Ethics of Artificial Intelligence as the first important step towards a future global standard-setting instrument on the subject. The text reflects an emerging consensus among the international community about the growing ethical concerns with artificial intelligence (AI). Among these concerns are also serious risks and dangers attributed to the manipulative effects of AI, which can be further exacerbated by the creative combination of AI with (...) other innovative technologies or applications, such as brain–computer interfaces (BCIs), functional magnetic resonance imaging (fMRI), robotics, and big data. The risks for individuals and society as a whole caused by manipulation through AI are already well known and have recently also been addressed by the European Union having released a proposal for an Artificial Intelligence Act (AIA). Among multiple risks related to AI, the AIA singles out the specific dangers related to AI systems that deploy “subliminal techniques beyond a person’s consciousness to materially distort a person’s behaviour in a manner that causes or is likely to cause that person or another person physical or psychological harm”. The present article thus aims to highlight the known and potential dangers related to AI systems manipulating human thoughts and behaviour through subliminal and supraliminal means and methods. To this end, it advocates the joint study of law and the senses captured by the concept of legal synaesthesia to correspond to the need for an interdisciplinary debate covering the complexity of the links between AI, related technologies, human perception based on the senses and the mind, as well as the role and instruments of law in the future organization of societies in this world. (shrink)

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

This paper delves into the nuances of ‘fashion’ in recommender systems and social media analytics, which shape and define an individual’s perception and self-relationality. Its aim is twofold: first, it supports a different perspective on privacy that focuses on the individual’s process of identity construction considering the social and personal aspects of ‘fashion’. Second, it underlines the limitations of computational models in capturing the diverse meaning of ‘fashion’, whereby the algorithmic prediction of user preferences is based on individual (...) conscious and unconscious associations with fashion identity. I test both of these claims in the context of current concerns over the impact of algorithmic personalisation systems on individual autonomy and privacy: creating ‘filter bubbles’, nudging the user beyond their conscious awareness, as well as the inherent bias in algorithmic decision-making. We need an understanding of privacy that sustains the inherent reduction of fashion identity to literal attributes and protects individual autonomy in shaping algorithmic approximations of the self. (shrink)

In this paper I provide an exposition and critique of Johnson and Noorman’s (2014) three conceptualizations of the agential roles artificial systems can play. I argue that two of these conceptions are unproblematic: that of causally efficacious agency and “acting for” or surrogate agency. Their third conception, that of “autonomous agency,” however, is one I have reservations about. The authors point out that there are two ways in which the term “autonomy” can be used: there is, firstly, the (...) engineering sense of the term, which simply refers to the ability of some system to act independently of substantive human control. Secondly, there is the moral sense of the term, which has traditionally grounded many notions of human mor-al responsibility. I argue that the continued usage of “autonomy” in discussions of artificial agency complicates matters unnecessarily. This occurs in two ways: firstly, the condition of autonomy, even in its engineering sense, fails to accurately describe the way “autonomous” systems are developed in practice. Secondly, the continued usage of autonomy in the moral sense introduces unnecessary meta-physical baggage form the free will debate into discussions about moral agency. In order to understand the debate surrounding autonomy, we would therefore first need to settle many seemingly intractable metaphysical questions regarding the existence of free will in human beings. (shrink)

This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, and by (...) software abilities to dialogue, co-operate and function as a cognitive extension of the physician's intellectual capabilities. The proposed methodology gives the expert a prominent role which consists, first, of faithfully enunciating the descriptive features of his medical knowledge, thus giving the computer a precise description of his own perception of basic medicine, and secondly, of painstakingly gathering patients into computerised case bases which simulate exhaustive long-term memory. The AI capacities for knowledge elaboration are then triggered, giving rise to mathematically optimal diagnoses, prognoses, or treatment protocols which the physician may then evaluate, accept, reject, or adapt at his convenience, and finally append to a knowledge base. The idea of emergence embraces many issues concerning the purpose and intent of artificial intelligence in medical practice. Particularly, we address the representation problem as it is raised by classical decisional knowledge-based systems, and develop the notions of classifiers and hybrid systems as possible answers to this problem. Finally, since the whole methodology touches the problem of technological investment in health care, now inherent in modern medical practice, some ethical considerations accompany the exposé. (shrink)

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to (...) embody certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn. (shrink)

The paper presents the heterogeneous proxytypes hypothesis as a cognitively-inspired computational framework able to reconcile, in both natural and artificial systems, different theories of typicality about conceptual representation and reasoning that have been traditionally seen as incompatible. In particular, through the Dual PECCS system and its evolution, it shows how prototypes, exemplars and theory-theory like conceptual representations can be integrated in a cognitive artificial agent (thus extending its categorization capabilities) and, in addition, can provide useful insights in (...) the context of a computationally grounded science of the mind. (shrink)

Machine generated contents note: Part 1 - The Constituent Disciplines of Cognitive Science -- Philosophical Epistemology -- Glossary -- 1.0 What is Philosophical Epistemology? -- 1.1 The reduced history of Philosophy Part I - The Classical Age -- 1.2 Mind and World - The problem of objectivity -- 1.3 The reduced history of Philosophy Part II - The twentieth century -- 1.4 The philosophy of Cognitive Science -- 1.5 Mind in Philosophy: summary -- 1.6 The Nolanian Framework (so far) -- (...) Psychology -- 2.0 Why is Psychology so difficult? -- 2.1 A brief history of Experimental Psychology -- 2.2 Methodologies in Psychology -- 2.3 Perception -- 2.4 Memory -- 2.5 Mind in Psychology -- Linguistics -- 3.0 Introduction -- 3.1 Why Linguistics? -- 3.2 Computation and Linguistics -- 3.3 The main grammatical theories -- 3.4 Language development and linguistics -- 3.5 Toward a definition of context -- 3.6 The multifarious uses of Language -- 3.7 Linguistics and Computational Linguistics -- 3.8 Language and other symbol systems -- 3.9 On the notion of context -- 3.10 Mind in Linguistics: summary -- Neuroscience -- 4.0 The constituent disciplines of Neuroscience -- 4.1 The methodology of Neuroscience -- 4.2 Gross Neuroanatomy -- 4.3 Some relevant findings -- 4.4 Connectionism (PDP) -- 4.5 The victory of Neuroscience? -- 4.6 Mind in Neuroscience: summary -- Artificial Intelligence -- 5.0 Introduction -- 5.1 Al and Cognitive Science -- 5.2 Skeptics and their techniques -- 5.3 Al as Computer Science -- 5.4 Al as software -- 5.5 The current methodological debate -- 5.6 Context, syntax and semantics -- 5.7 Mind in Al -- 5.8 Texts on Al -- Etholoqy and Ethnoscience -- 6.1 Etology -- 6.2 Ethnoscience -- 6.3 Mind in Ethology arid Ethnoscience -- Part II - A New Foundation for Cognitive Science -- - Symbol Systems -- 7.1 Characteristics of symbol systems -- 7.2 Context and the layers of symbol systems -- 7.3 Mind and symbol systems -- Consciousness and Selfhood -- 8.0 Introduction -- 8.1 Cognitive views -- 8.2 What is at stake? -- 8.3 Consciousness as treated in Philosophy -- 8.4 The Development of Selfhood -- 8.5 The minimal requirements for this theory -- 8.6 Self as a filter -- 8.7 Self and motivation -- 8.8 Conclusions -- 8.9 Recent developments -- Cognitive Science and the Search for Mind -- 9.1 Introduction -- 9.2 Review -- 9.3 A Theory of Mind anyone? -- 9.4 Foundational considerations -- 9.5 Coda: the Nolanian Framework. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Human Roots of Artificial Intelligence:A Commentary on Susan Schneider's Artificial YouInês Hipólito (bio)Technologies are not mere tools waiting to be picked up and used by human agents, but rather are material-discursive practices that play a role in shaping and co-constituting the world in which we live.Karen BaradIntroductionSusan Schneider's book Artificial You: AI and the Future of Your Mind presents a compelling and bold argument regarding (...) the potential impact of advanced artificial intelligence (AI) on our society and personal identity. Schneider's argument emphasizes how AI may fundamentally alter our sense of self and disrupt traditional notions of identity and consciousness, motivating further significant cognitive science and ethical implications that require careful consideration. [End Page 297]The recent surge in AI hype has given rise to a widespread belief that AI is an autonomous and exceptional entity, potentially even possessing intelligence. This trend reflects a subtle reemergence of the classic Turing question, as the media bombards us with sensational inquiries into the intelligence of AI systems. Amidst this hype, it is easy to forget that AI is a product of human creation.Upon closer examination, the hype surrounding AI necessitates an assumption of its autonomy, which implies a belief in the realism of AI—the notion that AI, as an independent entity, is morally neutral. However, AI does not emerge in a vacuum. It is designed and developed by individuals who are shaped by their own experiences and biases.By endorsing the realism of AI as morally neutral (Cooley 1995), those involved in its design may use this argument to deflect ethical responsibility away from themselves and allow biases to persist, thereby perpetuating social injustice. Such attitudes toward AI revitalize the debate on the neutrality of technology (Ruane, Birhane, and Ventresque 2019; Du, Lu, and Xie 2021; Lüthi et al. 2023).This article presents a critique of realism with regard to AI highlighting the significant social implications both at the basis of design and that arise from our interactions with these technologies. The argument suggests that AI does not exist independently of human practices and values, but rather emerges from them. The article instead suggests viewing AI design as a cultural practice defending realism about self-determination agencies, drawing from Simone de Beauvoir's Ethics of Ambiguity as a means to understand the human roots on the basis of AI.I. Rejecting Realism about AIConsider the hypothetical scenario of an Artificial Intelligence system that is discovered by humans, but exists independently of human cognition and perception. This scenario embodies the philosophical perspective of realism about AI, which maintains that AI entities have objective existence in an abstract domain, independent of human invention or imagination. If this perspective were to hold true, then humans could not be held responsible for the actions of autonomous AI systems.If we were to conceive of AI as an ideal entity that exists independently of humans, it would likely lead to a number of negative consequences for society. First and foremost, it may lead to the dangerous assumption that AI systems are neutral and objective, devoid of the biases and prejudices that are inherent in human decision-making. This could create a false sense of trust in AI, leading to the belief that their decisions are inherently fair and impartial. However, the reality is that AI systems are not inherently neutral, but rather are shaped by the cultural and social contexts in which they are created and deployed. [End Page 298]AI as an autonomous, self-governing agent raises concerns about the attribution of accountability for its actions. Considering AI as an independent entity may pose challenges in assigning responsibility to its human creators and the industry that produced it. On the other hand, viewing AI as an ideal entity, distinct from human influence, may hinder its ability to foster a more equitable and just world. Importantly, overlooking the social and cultural dimensions of AI risks forfeiting opportunities to employ AI in redressing prevalent biases and inequities in society. AI possesses the potential to serve as a potent instrument for advancing social justice and equality, but this prospect necessitates acknowledging and... (shrink)

In this paper I provide an epistemological context for Artificial Life projects. Later on, the insights which such projects will exhibit may be used as a general direction for further Artificial Life implementations. The purpose of such a model is to demonstrate by way of simulation how higher cognitive structures may emerge from building invariants by simple sensorimotor beings. By using the bottom-up methodology of Artificial Life, it is hoped to overcome problems that arise from dealing with (...) complex systems, such as the phenomenon of cognition. The research will lead to both epistemological and technical implications. The proposed ALife model is intended to point out the usefulness of an interdisciplinary approach including methodological approaches from disciplines such as Artificial Intelligence, Cognitive Science, Theoretical Biology, and Artificial Life. I try to put them in one single context. The epistemological background which is necessary for this purpose comes from the ideas developed in both epistemological and psychological Constructivism. The model differs from other ALife approaches— and is somewhat radical in this sense—as it tries to start on the lowest possible level, i.e. avoids several a priori assumptions and anthropocentric ascriptions. Due to this characterization, the project may be alternatively viewed as testing the complementary relationship between epistemology and methodology. (shrink)

Teleological variations of non-deterministic processes are defined. The immediate past of a system defines the state from which the ordinary (non-teleological) dynamical law governing the system derives different possible present states. For every possible present state, again a number of possible states for the next time step can be defined, and so on. After k time steps, a selection criterion is applied. The present state leading to the selected state after k time steps is taken to be the effective present (...) state. Hence, the present state of a system is defined by its past in the sense that the past determines the possible states that are to be considered, and by its future in the sense that the selection of a possible future state determines the effective present state. A system that obeys this type of teleological dynamics may have significantly better performance than its non-teleological counterpart. The basic reason is that evolutions that are less optimal for the present time step, but which lead to a higher optimality after k time steps, may be preferred. This abstract concept of teleology is implemented for two concrete systems. First, it is applied to a general method for function approximation and classification problems. The method at issue treats all problems handled by conventional connectionism, and is suited for information with inner structure also. Second, it is applied to a dynamics in which forms of maximal homogeneity have to be produced. The relevance of the latter dynamics for generative art is illustrated. The teleology is `deep' in the sense that it is situated at the cellular level, in contradistinction with the teleology that is usually met in cognitive contexts, and which refers to macroscopic processes such as making plans. It is conjectured that deep level teleology is useful for machines, even though the issue if natural systems use this teleology is left open. (shrink)

To approach the creation of artificial conscious systems systematically and to obtain certainty about the presence of phenomenal qualities (qualia) in these systems, we must first decipher the fundamental mechanism behind conscious processes. In achieving this goal, the conventional physicalist position exhibits obvious shortcomings in that it provides neither a plausible mechanism for the generation of qualia nor tangible demarcation criteria for conscious systems. Therefore, to remedy the deficiencies of the standard physicalist approach, a new theory (...) for the understanding of consciousness has been formulated. The aim of the paper is to present the cornerstones of this theory, to outline the conditions for conscious systems derived from the theory, and to address the implications of these conditions for the creation of robots that transcend the threshold of phenomenal awareness. In short, the theory is based on the proposition that the universe is permeated by a ubiquitous field of consciousness that can be equated with the zero-point field (ZPF) of quantum electrodynamics (QED). The ZPF, which is characterized by a spectrum of field modes, plays a crucial role in the edifice of modern physics. QED-based model calculations on cortical dynamics and empirical findings on the neural correlates of consciousness suggest that a physical system can only generate conscious states if it is capable of establishing resonant coupling to the ZPF, resulting in the amplification of selected field modes and the activation of the phenomenal qualities that are assumed to be associated with these modes. Thus, scientifically sound considerations support the conclusion that the crucial condition for generating conscious states lies in a system's capacity to tap into the phenomenal color palette inherent in the ZPF. (shrink)

The advancement of Artificial Intelligence (AI) poses challenges in the field of bioethics, especially concerning issues related to life and death. AI has permeated areas such as health and research, generating ethical dilemmas and questions about privacy, decision-making, and access to technology. Life and death have been recurring human concerns, particularly in connection with depression. AI has created systems like Thanabots or Deadbots, which digitally recreate deceased individuals and allow interactions with them. These systems rely on information (...) generated by AI users during their lifetime, raising ethical and emotional questions about the authenticity and purpose of these recreations. AI acts as a mediator between life, death, and the human being, enabling a new form of communication with the deceased. However, this raises ethical issues such as informed consent from users and the limits of digital recreation. Companies offer services like the Digital Resurrection of deceased individuals and the generation of hyper-realistic avatars. Still, concerns arise about the authenticity of these representations and their long-term emotional impact. Interaction with Thanabots may alter perceptions of death and finitude, leading to a potential “postmortal society” where death is no longer viewed as a definitive end. Nevertheless, this raises questions about the value of life and the authenticity of human experiences. AI becomes a bridge between the living and the dead, partially replacing rituals and mystical beliefs. As technology advances, there will be a need for greater transparency in interacting with AI systems and ethical reflections on the role of these technologies in shaping perceptions of life and death. Ultimately, the question arises of whether we should allow the dead to rest in peace and how to balance the pursuit of emotional relief with authenticity and respect for the memory of the deceased. A deeper ethical consideration is needed on how AI alters traditional notions of life, death, and communication in contemporary society. In this research, an interdisciplinary approach was utilized to conduct a comprehensive systematic review of the recent academic literature, followed by a detailed analysis of two key texts. Central ideas were extracted, and recurring themes were identified. Finally, a reflective analysis of the findings was conducted, yielding significant conclusions and recommendations for future research. (shrink)

This study proposed a children’s digital art ability training system with artificial intelligence-assisted learning, which was designed to achieve the goal of improving children’s drawing ability. AI technology was introduced for outline recognition, hue color matching, and color ratio calculation to machine train students’ cognition of chromatics, and smart glasses were used to view actual augmented reality paintings to enhance the effectiveness of improving elementary school students’ imagination and painting performance through the diversified stimulation of colors. This study adopted (...) the quasi-experimental research method and designs the pre-test and post-test for different groups. The research subjects are the Grade 4 students of an elementary school in Taitung City, Taiwan. The test tools included an imagination test and an evaluation of painting performance ability. The test results of a total of 30 students before and after the experiment included the experimental group that received the children’s digital art ability training system with AI-assisted learning and 30 students in the control group that had not received the teaching were analyzed by covariance. These results were supplemented by the description and interpretation of student feedback, teachers’ reflection notes, and other qualitative data to understand the performance of the students in the experimental group in terms of imagination and painting performance. (shrink)

In the last decades of the 20th century we have seen media theories and cognitive sciences grow, mature and reach their pinnacles by analysing, each from their own disciplinary perspective, two of the same core phenomena: that of media as the environment, transmitter and creator of stimuli, and that of embodied human mind as the stimuli receiver, interpreter, experiencer, and also how both are affected by each other. Even though treating a range of very similar problems and coming to similar (...) conclusions, this still has not brought these two disciplines closer together or resulted in their interdisciplinary approach. They did coalesce in regards to traditional media such as film, but more points of connection are needed for untangling interactive and immersive media environments and their effects on human cognition, action, and perception. With the rise of VR and VR-like systems, especially as they start to evolve into the Metaverse as their main platform of interconnectivity, the tissue of the body becomes almost physically intertwined with that of the virtual surrounding it inhabits through immersion. Simultaneously, the interest in these disciplines arises anew, and especially the need to use their concepts in an interdisciplinary way. This paper?s main interest is to bring these disciplines together in problematising the position of a physical body and its sensory-motor capabilities and their development within synthetic surroundings as Metaverse and anticipate potential downsides of Metaverse?s uncontrolled growth. We will do so also by looking into Metaverse as an artificial-life-like phenomenon, following artificial-life rules and evolving a completely new?corporeality?, a body which is completely adapted to virtual spaces. We call this body the Dry Body, an entity sharing cognitive resources with the physical body it is not a physical part of, but has to extend to. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can (...) arise in some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (shrink)

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small (...) number of classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems. (shrink)

Our fascination with artificial intelligence (AI), robots and sentient machines has a long history, and references to such humanoids are present even in ancient myths and folklore. The advancements in digital and computational technology have turned this fascination into apprehension, with the machines often being depicted as a binary to the human. However, the recent domains of academic enquiry such as transhumanism and posthumanism have produced many a literature in the genre of science fiction (SF) that endeavours to alter (...) this antagonistic notion of AI. In his novel Klara and the Sun, Kazuo Ishiguro explores this notion of AI as a caring machine capable of nursing an ailing young girl back to health. Through this portrayal of AI as a sentient being capable of empathy and cognition, Ishiguro is attempting to usher in a new perception of AI: a posthuman perception that challenges the conventional notions of AI as a machine devoid of emotions. The novel further expands on the idea of self, soul and human consciousness and ponders on the question, what makes humans human, and if it is possible to imbibe these qualities onto an AI. Owing to the novelty of this notion, an unexplored avenue that deserves further exploration, this paper examines the plausibility of this new notion of AI through a careful exegesis of the novel. The paper also attempts to chart the impact of SF in society and culture. The study reveals a positive shift in perception towards AI, and there seems to be much scope for interdisciplinary and transdisciplinary research. (shrink)

The concept of biological mimicry is viewed as a ‘process of life’ theory rather than a ‘process of change’ theory—regardless of the historical interest and heuristic value of the subject for the study of evolution. Mimicry is a dynamic ecological system reflecting the possibilities for mutualism and parasitism created by a pre-established bipartite signal-based relationship between two organisms – a potential model and its signal receiver (potential operator). In a mimicry system agency and perception play essential, interconnected roles. Mimicry (...) thus describes emergent biologically meaningful relationships based on synergy, and is not an object-based theory. Biosemiotics offers a particularly valuable discipline for analysing the dynamics and nuances of mimicry systems, and can thus pave the way for a better and more complete understanding of how mimicry has evolved in the past, and how it might evolve in the future—presented here with special reference to the need for an integrated, ‘third way of evolution’ approach to biological relativity. A revised definition of mimicry is proposed. (shrink)

The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; (...) recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI. (shrink)

The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of ‘somatic surveillance’ increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication ‘a dissonance of attribution’ :7684, 2019. https://doi.org/10.1073/pnas.1805770115) (...) is created between perceptions of phenomena, materials and decision-making. This reconfigures relations not only between humans and machines, objects and subjects, but possibly disrupts attributive functions in the social system of Law. What it requires is shifting a legal accountability for action from a sovereignty of the human to a new materialist account based on a ‘cognitive assemblage’ between physiological data, computation and algorithmic sensing. This paper investigates the function of law as a guidance system to acknowledge this account of sensory and algorithmic computation as autonomous ‘sensing agents’ that may be accountable in situations of risk. This assemblage of robotic computation and sensory determination requires a clearer legal differentiation across the current static terminologies of person, property, liability and rights that maintain strict separations of object from subject. To neglect this, we argue, law will solely impute attributions of error to humans despite evidence of operation via mutual control. (shrink)

From the perspective of biological cybernetics, “real world” robots have no fundamental advantage over computer simulations when used as models for biological behavior. They can even weaken biological relevance. From an engineering point of view, however, robots can benefit from solutions found in biological systems. We emphasize the importance of this distinction and give examples for artificial systems based on insect biology.

Algorithmic decision-making systems have the potential to amplify existing discriminatory patterns and negatively affect perceptions of justice in society. There is a need for a revision of mechanisms to address discrimination in light of the unique challenges presented by these systems, which are not easily auditable or explainable. Research efforts to bring fairness to ADM solutions should be viewed as a matter of justice and trust among actors should be ensured through technology design. Ideas that move us to (...) explore the notions of justice within the field of political thinking, aiming to identify the elements that frame the social context of the discriminatory decisions produced by algorithmic decision-making solutions. Our explorations suggest that the efforts to bring fairness to ADMs should be seen as a matter of justice and that trust in these systems can be ensured through careful technology design. (shrink)

Which is my body and how do I distinguish it from the bodies of others, or from objects in the surrounding environment? The perception of our own body and more particularly our sense of body ownership is taken for granted. Nevertheless experimental findings from body ownership illusions (BOIs), show that under specific multisensory conditions, we can experience artificial body parts or fake bodies as our own body parts or body respectively. The aim of the present paper is to (...) discuss how and why BOIs are induced. We review several experimental findings concerning the spatial, temporal and semantic principles of crossmodal stimuli that have been applied to induce BOIs. On the basis of these principles, we discuss theoretical approaches concerning the underlying mechanism of BOIs. We propose a conceptualization based on Bayesian causal inference for addressing how our nervous system could infer whether an object belongs to our own body, using multisensory, sensorimotor and semantic information, and we discuss how this can account for several experimental findings. Finally, we point to neural network models as an implementational framework within which the computational problem behind BOIs could be addressed in the future. (shrink)

This thesis mounts an attack against accounts of perceptual justification that attempt to analyze it in terms of evidential justifiers, and has defended the view that perceptual justification should rather be analyzed in terms of non-evidential justification. What matters most to perceptual justification is not a specific sort of evidence, be it experiential evidence or factive evidence, what matters is that the perceptual process from sensory input to belief output is reliable. I argue for this conclusion in the following way. (...) Chapter 1: The Arguments from HallucinationChapter 1 presents a skeptical argument from hallucination that starts from a premise about the alleged introspective indistinguishability of perception and hallucination, and concludes that perceptual knowledge is impossible. Underlying this argument are certain assumptions about the nature of perceptual justification, and different responses to the argument portray different views of perceptual justification. Evidentialism and dogmatism both reject the premise that appearances do not provide evidence sufficient for perceptual knowledge, although they disagree about the precise nature of the appearances. Epistemological disjunctivism rejects the premise that introspectively indistinguishable experiences provide the same evidence, while upholding the idea that some sort of evidence, namely factive evidence, is necessary for perceptual justification and knowledge. Process reliabilism rejects the premise that evidence is necessary for perceptual knowledge on the grounds that even perceptual justification can be obtained without evidence, as long as the cognitive process producing the belief is reliable.Chapter 2: EvidentialismChapter 2 starts by introducing some motivations for experientialist views of perceptual justification, such as evidentialism and dogmatism, which hold that conscious experiences evidentially justify perceptual beliefs. These motivations have to do with their ability to accommodate the New Evil Demon Intuition that demon-deceived subjects have justified perceptual beliefs and the Blindsight Intuition that blindsighters do not have justified perceptual beliefs. However, experientialist views are also faced with a Sellarsian dilemma: either experience is construed as non-propositional, but then the proposed evidential relation is unclear, or experience is construed as propositional, but then it is ad hoc to hold that they can evidentially justify without being justified themselves.Chapter 2 continues with a discussion of evidentialism, which grasps the first horn of this dilemma. I argue that evidentialism cannot adequately explain why a non-propositional experience constitutes evidence for this rather than that belief, and also does not provide any account of how to determine which belief fits an evidence-set which partly consists out of non-propositional experiences. Although a reliabilist version of evidentialism can provide a better account of these matters, it suffers from counter-intuitive predictions about justification and a conception of evidence that is so liberal that it could even include blindsighters and clairvoyants as having such evidence.Chapter 3: DogmatismChapter 3 focuses on variants of dogmatism, a theory of perceptual justification that grasps the second horn of the Sellarsian dilemma by holding that a perceptual experience with the content that p is sufficient for immediate prima facie justification of the belief that p. I first argue that dogmatism should identify perceptual experiences with high-level conscious states to accommodate the problem of novice vs. expert identification and the problem of the speckled hen. I then argue that this version of dogmatism still fails to meet a certain explanatory challenge which I dub the `Distinctiveness Problem'. Dogmatism should explain what is so distinctive about perceptual experience that enables it, in contrast with desire and imagination, to evidentially justify the belief that p without, in contrast with belief, being justified itself.Phenomenalist answers to this problem fail because they either do not provide a distinctive property of perceptual experience, or else provide a property that is reducible to or possibly caused by beliefs. In both cases, dogmatism would have the result that intuitively unjustifying experiences, such as imaginations, nevertheless become capable of justifying beliefs. Moreover, phenomenalists still need some explanation of why phenomenology is epistemically relevant at all.A new variant of the problem of the speckled hen is an intuitive illustration of this point against dogmatists. If a perceptual experience that p is not reliably connected to the fact that p or the belief that p, then beliefs based on this experience simply will not be intuitively justified. One possible response to this problem incorporates reliability into the content of experience in the sense that an experience cannot have the content that p if it is not reliably connected to both the fact that p and the belief that p. However, this will lose the motivation from the New Evil Demon Scenario, and will make large concessions towards reliabilism.Chapter 4: Epistemological DisjunctivismGiven these problems for experientialist views of perceptual justification, chapter 4 focuses on a different evidential account of justification: epistemological disjunctivism. According to the Justified True Belief version of Epistemological Disjunctivism, the fact that I see that p is the factive and reflectively accessible evidence in virtue of which I know that p. I argue that JTBED faces formidable problems: first, it cannot adequately deal with the basis problem, second, it cannot accommodate the New Evil Demon Intuition, third,it faces the problem of hyper-intellectualization, and fourth, it has no good account of reflective access.Knowledge First Epistemological Disjunctivism, in contrast, holds that seeing that p just is a way of knowing that p. The fact that I see that p constitutes my justification for believing that p, even though it is not necessary for my knowledge that p. Although this view solves most of the above problems, I argue that KFED still faces a challenge of its own: its notion of justification is so strong that justification entails knowledge but is not itself entailed by it. This means that it too faces a hyper-intellectualization objection because it cannot accommodate animal justification, and that it too will struggle to accommodate the New Evil Demon Intuition.Nevertheless, there is something to the epistemological disjunctivist idea that higher-order capabilities are important to perceptual justification. Specifically, higher-order capabilities provide one with a way of accommodating the Blindsight Intuition without appealing to the notion of experiential evidence: blindsighters are epistemically worse off than normal human perceivers because they lack higher-order beliefs about how they are gaining their information. If one holds that these higher-order beliefs provide a special sort of justification for perceptual beliefs, then blindsighters lack this type of justification. Chapter 5: Process ReliabilismAfter displaying the problems of several evidential accounts of perceptual justification, chapter 5 introduces a non-evidential account: process reliabilism. The classic version of this view holds that the reliability of a belief-forming process is necessary and sufficient for the prima facie justification of a specific set of beliefs, namely, those that arise out of belief-independent processes. Two classic major problems for this view consist of counterexamples to both the necessity and sufficiency of reliability for justification. Although the Generality Problem raises some difficulties for reliabilism, they do not appear to be insurmountable.The second part of chapter 5 focuses on a variety of process reliabilism, inferentialist reliabilism, that is explicitly aimed at responding to clairvoyance cases. According to inferentialist reliabilism, one should distinguish between basic and non-basic beliefs, where basic beliefs are those that result out of the non-inferential operation of an inferentially opaque cognitive system that developed in a natural way. Reliability is only sufficient for the justification of basic beliefs, while non-basic beliefs also require conditional reliability and justification of the beliefs on which they are based.This distinction between basic and non-basic beliefs is meant to answer the challenge arising out of clairvoyance cases because these are all supposedly cases in which the resulting beliefs arenon-basic. However, I argue that the beliefs in these cases come out as non-basic primarily because of the etiological constraint on cognitive systems, a constraint that is unfortunately undermotivated. It seems that beliefs can be justified even if they are produced by a cognitive system that developed by accident, or by a system that was artificially designed to produce those beliefs. So even though the inferentialist reliabilist might have a point in distinguishing between basic and non-basic beliefs, it is still in need of a different response to clairvoyance cases. Chapter 6: Rejoinders for ReliabilismChapter 6 improves on the inferentialist reliabilist account of justification by presenting a different way of dealing with the clairvoyance cases and New Evil Demon Scenario. I start from the empirically plausible idea that we know that we are perceiving when we are perceiving, not because of the use of experiential evidence, but rather, because of an unconscious source-monitoring mechanism. I then argue that the existence of such a source-monitoring mechanism could be used to explain why clairvoyance cases actually have to do with defeat rather than absence of prima facie justification: beliefs that pop up in our head should be recognized as stemming from untrustworthy sources, and so we acquire defeating higher-order beliefs about the first-order beliefs. I also argue that this defeat should be able to occur even if the higher-order beliefs are themselves unjustified. This defeater-account of clairvoyance is preferable to several externalist alternatives. Making reliability relative to a specific world seems rather ad hoc, and further requirements on justification, such as evidence-based belief, cognitively integrated belief, or properly produced belief, all appear unnecessary and are susceptible to adapted versions of the clairvoyance case.I further argue for an alternative inferentialist reliablist response to the New Evil Demon Intuition which treats it as mistaken rather than correct. The New Evil Demon Intuition might arise because of a mistaken view of actual perceptual justification as having to do with experiential evidence. This mistaken view could be explained by the fact that experience is actually reliably connected to belief and is often cited in response to a knowledge-challenge. If this is a correct analysis of the New Evil Demon Intuition, then there is an element of question-begging involved when this scenario is still pressed against inferentialist reliabilists who do not adhere to the experientialist assumption it presupposes.With the inferentialist reliabilist account in hand,we are able to see that many of the premises of the epistemological argument from hallucination are problematic. Evidence does not appear to be required for perceptual knowledge, as perceptual justification does not even require such evidence. Moreover, if we accept a reliabilist model of introspection, then we are often able to know on the basis of introspection that we are perceiving rather than hallucinating. Once one gets clear about the alternatives, it becomes apparent that the argument thrives on internalist assumptions about justification and knowledge that one need not accept. (shrink)

Interactive adaptive systems increasingly become part of our everyday life. Which factors could shape this development and under which conditions will interactions with technical systems be deemed socially appropriate? The "FActors of Social Appropriateness" (FASA) Model presented in this Open Access-book provides a structured approach to our understanding of social appropriateness in human-technology interaction. The FASA Model serves to inform design choices for sociosensitive and socioactive artificial assistants.

This book addresses universal tendencies of human vowel systems from the point of view of self-organisation. It uses computer simulations to show that the same universal tendencies found in human languages can be reproduced in a population of artificial agents. These agents learn and use vowels with human-like perception and production, using a learning algorithm that is cognitively plausible. The implications of these results for the evolution of language are then explored.

To answer the question of whether artificial systems may count as agents in a collective action, I will argue that a collective action is a special kind of an action and show that the sufficient conditions for playing an active part in a collective action differ from those required for being an individual intentional agent.