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

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective nature of ethical decision-making. (...) The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective nature of ethical decision-making. (...) The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

As artificial intelligence (AI) integrates across social domains, prevailing technical paradigms often overlook human relational needs vital for cooperative resilience. Alternative pathways consciously supporting dignity and wisdom warrant consideration. Integrating seminal insights from virtue and care ethics, this article delineates the following four cardinal design principles prioritizing communal health: (1) affirming the sanctity of life; (2) nurturing healthy attachment; (3) facilitating communal wholeness; and (4) safeguarding societal resilience. Grounding my analysis in the rich traditions of moral philosophy, I argue that (...) these principles scaffold sustainable innovation trajectories that consciously center shared welfare advancement over detached technical capabilities or efficiency benchmarks alone. Elucidating connections with pioneering initiatives demonstrates fragments of this vision taking embryonic shape, yet pervasive adoption remains largely aspirational to date. Fulfilling dignity-based artificial intelligence demands ongoing collective commitment beyond firms’ profit motives or governance proceduralism. My conclusions urge technology policies and priorities directed toward empowering the vulnerability of people rather than controlling the optimization of systems. (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

The article considers the method of factor cluster analysis which allows automatically retrain the onboard recognition system of an unmanned aerial system. The task of informational synthesis of an on-board system for identifying frames is solved within the information-extreme intellectual technology of data analysis, based on maxi- mizing the informational ability of the system during machine learning. Based on the functional approach to modeling cognitive processes inherent to humans during forming and making classification decisions, it was (...) proposed a categorical model in the form of a direct graph. According to this model, the algorithmic support of the information extreme factor cluster analysis is developed. It allows automatically retrain the system when expanding the alphabet of recognition classes. According to this algorithm, the on-board recognition system preliminarily carries out the information-extremal machine learning of recognition classes of relatively low power. When new classes appear, their unclassified structured recognition attribute vectors form additional learning matrixes. After reaching a representational volume, additional learning matrix joins the input learning matrix and the on-board recognition system is retrained. Forming additional learning matrixes of new recognition classes is carried out by the agglomerative algorithm of cluster analysis of unclassified vectors by k-means clustering. As a criterion of optimizing machine-learning parameters, we used the modified Kullback criterion which is a functional of the exact characteristics of classification solutions. To increase the functional efficiency of factor cluster analysis, it is proposed to increase the depth of machine learning by optimizing the parameters of image processing frames. (shrink)

Decision theory has had a long-standing history in the behavioural and social sciences as a tool for constructing good approximations of human behaviour. Yet as artificially intelligent systems (AIs) grow in intellectual capacity and eventually outpace humans, decision theory becomes evermore important as a model of AI behaviour. What sort of decision procedure might an AI employ? In this work, I propose that policy-based causal decision theory (PCDT), which places a primacy on the decision-relevance of predictors and simulations of (...) agent behaviour, may be such a procedure. I compare this account to the recently-developed functional decision theory (FDT), which is motivated by similar concerns. I also address potentially counterintuitive features of PCDT, such as its refusal to condition on observations made at certain times. (shrink)

Artificial intelligence systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will function. This (...) means that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process. (shrink)

The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD, a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, (...) we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models. (shrink)

Public entities around the world are increasingly deploying artificial intelligence and algorithmic decision-making systems to provide public services or to use their enforcement powers. The rationale for the public sector to use these systems is similar to private sector: increase efficiency and speed of transactions and lower the costs. However, public entities are first and foremost established to meet the needs of the members of society and protect the safety, fundamental rights, and wellbeing of those they serve. Currently AI systems (...) are deployed by the public sector at various administrative levels without robust due diligence, monitoring, or transparency. This paper critically maps out the challenges in procurement of AI systems by public entities and the long-term implications necessitating AI-specific procurement guidelines and processes. This dual-prong exploration includes the new complexities and risks introduced by AI systems, and the institutional capabilities impacting the decision-making process. AI-specific public procurement guidelines are urgently needed to protect fundamental rights and due process. (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)

Theories of intelligence can be of use to neuroscientists if they: 1. Provide illuminating suggestions about the functional architecture of neural systems; 2. Suggest specific models of processing that neural circuits might implement. The objective of our session was to stand back and consider the prospects for this interdisciplinary exchange.

Introduction. Innovationism in various areas of society has changed both the natural and social environment. The change speed in the new infor- mation and communication field is the reason for many questions related to studying the problems of society and the machine, finding out the place of artificial intelligence in social relations. These pro- cesses stimulated the philosophical research, the subject of which was man, modern technologies, scenarios for the development of society, socio- cultural and political-legal forms of its organization. (...) Since the 90s of the twentieth century in the West and later in Russia, a transhumanistic concept has been formed, which connects man, society and technology in a special way. Representatives of transhumanism study the technical develop- ments of human immortality and build models of the perfect man, pursuing as a goal, his deliver- ance from suffering and disease. Transhumanistic perspectives actualize the need for a thorough, constructive and critical assessment of the possible advantages and risks of technological development, and its impact on the political and legal structure. In this regard, the ability to see and isolate the features of this process and subsequent reflection is fundamentally important. The purpose of the article is to consider political and legal transformations in the context of the interaction of modern technologies and soci- ety through the prism of transhumanistic perspec- tives, to identify possible directions of philosophical reflection of political and legal modes of modernity. Methods. To achieve the research goal, general scientific methods of cognition were used: analysis and synthesis, induction, deduction, comparison, abstraction. The article also applied institutional- functional and system-structural approaches, the method of interpretation and content analysis. The analysis of public opinion and special labeling of artificial intelligence was carried out with the help of appealing to network content (social networks, blogs, and forums). Communication channels reproduce messages mentioning modern technolo- gies from the point of view of the philistine world- view based on stereotypes broadcast in society and the influence of popular culture. Content analysis revealed the relevance of the spectrum of labeling, relative to artificial intelligence systems, includ- ing the verbs “create”, “destroy”, “help”, “enslave”. Regarding the sphere of labor, law and politics, the positions differ, from outright alarmism to utopian images of social well-being. Scientific novelty of the study. The article analyzes the prospects of considering the phenom- enon of social transformations, the forms of their representation through political and legal views as exemplified by transhumanism. Results. The author attempts to give a comprehen- sive description of social changes in the field of public administration, legal regulation, legal per- sonality and legal responsibility, social equality of participants in legal relations through the specific refraction of these social phenomena in transhu- manistic ideas. The spectrum of the main problems in the sphere of interests of the transhumanistic worldview is listed. Conclusion. Turning to modernization strategies on the example of transhumanism is a natural and necessary need in the political and legal sphere, in particular. In the course of the analysis, a wide range of problems were identified that actualize the social and axiological and anthropological optics of the research. Diverse forms of transhumanistic political trends act as manifestations of socio-legal reflection on the intensification of innovation- ism, which is capable of radically changing human nature. (shrink)

A clear account of the principles of fuzzy logic-based design, from a computer/electronics engineering perspective. This pedagogical work incorporates current fuzzy logic techniques, emphasizing hardware/software design for fuzzy systems and fuzzy logic development tools.

It has frequently been recognized that the collection and use of accurate military intelligence was of fundamental importance for the success of Alexander's campaigns. No intelligent strategic or tactical decision can be made by any commander without advance knowledge of an enemy's location, strength, his capabilities and weaknesses, and the geography of the projected campaign. However, an analysis of the procedures Alexander used to obtain and evaluate intelligence has never been undertaken. This neglect is probably the result of the (...) scattered and unsatisfactory nature of the references to Alexander's intelligence procedures in our ancient sources. (shrink)

The question of whether AI systems such as robots can or should be afforded moral agency or patiency is not one amenable either to discovery or simple reasoning, because we as societies constantly reconstruct our artefacts, including our ethical systems. Consequently, the place of AI systems in society is a matter of normative, not descriptive ethics. Here I start from a functionalist assumption, that ethics is the set of behaviour that maintains a society. This assumption allows me to exploit the (...) theoretical biology of sociality and autonomy to explain our moral intuitions. From this grounding I extend to consider possible ethics for maintaining either human- or of artefact-centred societies. I conclude that while constructing AI systems as either moral agents or patients is possible, neither is desirable. In particular, I argue that we are unlikely to construct a coherent ethics in which it it is ethical to afford AI moral subjectivity. We are therefore obliged not to build AI we are obliged to. (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)

One of the most foundational and continually contested questions in the cognitive sciences is the degree to which the functional organization of the brain can be understood as modular. In its classic formulation, a module was defined as a cognitive sub-system with nine specific properties; the classic module is, among other things, domain specific, encapsulated, and implemented in dedicated neural substrates. Most of the examinations—and especially the criticisms—of the modularity thesis have focused on these properties individually, for instance by (...) finding counterexamples in which otherwise good candidates for cognitive modules are shown to lack domain specificity or encapsulation. The current paper goes beyond the usual approach by asking what some of the broad architectural implications of the modularity thesis might be, and attempting to test for these. The evidence does not favor a modular architecture for the cortex. Moreover, the evidence suggests that best way to approach the understanding of cognition is not by analyzing and modelling different functional domains in isolation from the others, but rather by looking for points of overlap in their neural implementations, and exploiting these to guide the analysis and decomposition of the functions in question. This has significant implications for the question of how to approach the design and implementation of intelligent artifacts in general, and language-using robots in particular. (shrink)

In this paper, the current AI view that emergent functionalities apply only to the study of subcognitive agents is questioned; a hypercognitive view of autonomous agents as proposed in some AI subareas is also rejected. As an alternative view, a unified theory of social interaction is proposed which allows for the consideration of both cognitive and extracognitive social relations. A notion of functional effect is proposed, and the application of a formal model of cooperation is illustrated. Functional cooperation shows the (...) role of extracognitive phenomena in the interaction of intelligent agents, thus representing a typical example of emergent functionality. (shrink)

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, (...) with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes. (shrink)

Over the past decades, due to the course towards digitalization of all areas of life, interest in modeling and creating intelligent systems has increased significantly. However, there are now a stagnation in the industry, a lack of attention to analog and bionic approaches as alternatives to digital, numerous speculations on “neuro” issues for commercial and other purposes, and an increase in social and environmental risks. The article provides an overview of the development of artificial intelligence (AI) conceptions toward increasing (...) the human likeness of machines: from the key ideas of A. Turing and J. von Neumann, who initiated the digitalization of society, to discussions about the definition of AI and the emergence of conceptions of strong and weak AI. Special attention is paid to the approach of A. Sloman, to ideas about the architecture and design of complex artificial systems are considered, which make it possible to “emotionally” expand the idea of weak/strong AI. In the article's section on the necessity and possibility of incorporating emotions into the architecture of AI, the authors reveal the goals and methodological limitations for creating an emotional artificial agent. In addition, the article briefly presents the main principles of the authors' architectural approach to the creation of emotional intellectual systems on the example of the cognitive-affective model of architecture, which allow modeling the impact of emotions on the cognitive processes involved in decision-making processes. The described architectural approach to modeling intelligent systems can be used as a conceptual basis for discussing and formulating a strategy for the development of neurocomputing, philosophy of artificial intelligence, and experimental philosophy, for developing innovative research programs, formulating and solving theoretical and methodological problems. (shrink)

Artificial intelligence systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5–10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation for medical error. It argues that the introduction of AI systems will provide an additional argument pointing towards no-fault compensation as the better legal solution to compensation for medical error in modern health care systems. The paper falls into four parts. The first part rehearses the main arguments for (...) and against no-fault compensation. The second explains why it is likely that AI systems will be widely introduced. The third part analyses why it is difficult to fit AI systems into fault-based compensation systems while the final part suggests how no-fault compensation could provide a possible solution to such challenges. (shrink)

Methods of improving the state and rate of progress within the domain of philosophy using collective intelligence systems are considered. By applying mASI systems superintelligence, debiasing, and humanity’s current sum of knowledge may be applied to this domain in novel ways. Such systems may also serve to strongly facilitate new forms and degrees of cooperation and understanding between different philosophies and cultures. The integration of these philosophies directly into their own machine intelligence seeds as cornerstones could further serve to reduce (...) existential risk while improving both ethical quality and performance. (shrink)

Methods of improving the state and rate of progress within the domain of philosophy using collective intelligence systems are considered. By applying mASI systems superintelligence, debiasing, and humanity’s current sum of knowledge may be applied to this domain in novel ways. Such systems may also serve to strongly facilitate new forms and degrees of cooperation and understanding between different philosophies and cultures. The integration of these philosophies directly into their own machine intelligence seeds as cornerstones could further serve to reduce (...) existential risk while improving both ethical quality and performance. (shrink)

The evolution of Intelligent Tutoring System (ITS) is the result of the amount of research in the field of education and artificial intelligence in recent years. English is the third most common languages in the world and also is the internationally dominant in the telecommunications, science and trade, aviation, entertainment, radio and diplomatic language as most of the areas of work now taught in English. Therefore, the demand for learning English has increased. In this paper, we describe the (...) design of an Intelligent Tutoring System for teaching English language grammar to help students learn English grammar easily and smoothly. The system provides all topics of English grammar and generates a series of questions automatically for each topic for the students to solve. The system adapts with all the individual differences of students and begins gradually with students from easier to harder level. The intelligent tutoring system was given to a group of students of all age groups to try it and to see the impact of the system on students. The results showed a good satisfaction of the students toward the system. (shrink)