Robot rights: Where science ends and fiction starts

Artificial intelligence (AI) has continued to disrupt the way tasks are being carried out, finding its way into almost all facets of human existence, and advancing the development of human society. The AI revolution has made huge and significant inroad into diverse industries like health, energy, transport, retail, advertising, et cetera. AI has been found to assist in carrying out tasks more quickly and efficiently too. Tasks which were hitherto difficult have been simplified significantly through the use of AI. Slow adoption in judiciary has however been reported, compared to other sectors. A lot of factors have been attributed to this, with AI bias being an issue of concern. Decisions emanating from courts have a significant impact on an individual’s private and professional life. It is thus imperative to identify and deal with bias in any judicial AI system in order to avoid delivering a prejudiced and inaccurate decision, thereby possibly intensifying the existing disparities in the society. This paper therefore surveys judicial artificial intelligence bias, paying close attention to types and sources of AI bias in judiciary. The paper also studies the trust-worthy AI, the qualities of a trust-worthy artificial intelligence system and the expectations of users as it is being deployed to the judiciary, and concludes with recommendations in order to mitigate the AI bias in Judiciary.

Artificial Intelligence (AI) is reshaping the way legal services are delivered, which includes arbitrators in Alternative Dispute Resolution (ADR) proceedings. This trend is being accelerated by the coronavirus (COVID-19) pandemic, with many disputes being conducted online. Previously, legal tasks have usually been associated with human lawyers; however, increasingly autonomous computer systems may soon replace arbitrators in the delivery of legal services in ADR proceedings. This article evaluates the extent to which the deployment of AI may augment arbitrators in ADR proceedings and provides an analysis of how much legal work may be effectively performed by AI in ADR proceedings. The central focus is on the capabilities of AI systems against human arbitrators and the requirement for human intervention and supervision of AI systems deployed in ADR proceedings. This article extends the debate on AI and arbitrators where the focus is on the augmentation of legal services in ADR with AI systems and the implications therein. The doctrinal approach is employed to conduct the study. The primary objective of this article is to evaluate the extent to which the deployment of AI systems may augment the efficiency and productivity of legal services in ADR and to highlight the proposed recommendations.

Artificial intelligence (AI) is said to be the next big phase in digitalization. There is a global ongoing race to develop, implement and make use of AI in both the private and public sector. The many responsibilities of governments in this race are complicated and cut across a number of areas. Therefore, it is important that the use of AI supports these diverse aspects of governmental commitments and values. The aim of this paper is to analyze how AI is portrayed in Swedish policy documents and what values are attributed to the use of AI. We analyze Swedish policy documents and map benefits, considerations and risks with AI into different value ideals, based on an established e-government value framework. We conclude that there is a discrepancy in the policy level discourse on the use of AI between different value ideals. Our findings show that AI is strongly associated with improving efficiency and service quality in line with previous e-government policy studies. Interestingly, few benefits are highlighted concerning engagement of citizens in policy making. A more nuanced view on AI is needed for creating realistic expectations on how this technology can benefit society.

Artificial Intelligence (AI) is an umbrella term used to describe machine-based forms of learning. This can encapsulate anything from Siri, Apple’s smartphone-based assistant, to Tesla’s autonomous vehicles (self-driving cars). At present, there are no set criteria to classify AI. The implications of which include public uncertainty, corporate scepticism, diminished confidence, insufficient funding and limited progress. Current substantial challenges exist with AI such as the use of combinationally large search space, prediction errors against ground truth values, the use of quantum error correction strategies. These are discussed in addition to fundamental data issues across collection, sample error and quality. The concept of cross realms and domains used to inform AI, is considered. Furthermore there is the issue of the confusing range of current AI labels. This paper aims to provide a more consistent form of classification, to be used by institutions and organisations alike, as they endeavour to make AI part of their practice. In turn, this seeks to promote transparency and increase trust. This has been done through primary research, including a panel of data scientists / experts in the field, and through a literature review on existing research. The authors propose a model solution in that of the Hierarchy of AI.

Artificial intelligence (AI) is already part of our everyday life. Nowadays, facial recognition and autonomous driving are common applications of AI. It has now gone on to invade the field of medicine, and many studies showed promising applications of AI in radiology, pathology, endoscopy, and so on. Artificial intelligence (AI) has been a disruptive innovation in all areas of medicine including surgery. Not only has it been applied for research purpose but also AI can already provide significant solutions in clinical settings. Attention is now turning to the potential of AI in the field of endoscopic surgery. It is essential to understand how machine learning (ML) and deep learning (DL) work, understand current applications in surgery, and prepare for the future of surgery in the era of AI.

Artificial intelligence (AI) is a set of technologies that allows robots to detect, understand, act, and learn at human-like levels. The vast bulk of the AI that surrounds us now is powered by ineffective AI. This sort of AI is demonstrated by IBM Watson, self-driving cars, and Apple's Siri. Machine learning includes deep learning as a subset which is commonly utilised in predictive modelling. It functions in the same manner that neurons do in our brains. Its structure and networking are also influenced by neural networks found in our brains. Since the 1950s, artificial intelligence (AI) has been applied in medicine. The use of artificial intelligence in ophthalmology is mostly focused on high-incidence illnesses such as diabetic retinopathy, glaucoma, age-related macular degeneration, and cataract. The chapter reviews research demonstrating AI enhancing medical diagnosis with a focus on diabetic retinopathy.

Artificial intelligence (AI) has been used mainly on education in some methods that contribute to the development of competencies and test systems. With the continued development of educational AI solutions, it is hoped that AI will help address the need for learning, education, and teaching. AI can enhance performance, personalization, and streamline administrative tasks in order to give teachers time and freedom to learn and adapt—uniquely human skills that would battle on machines. The AI dream of education is one where the best results for students are obtained, based on the best qualities of machinery and teachers. The development of curriculum based on the specific needs of individual students has been a concern for educators for many years, but the AI presents teachers with an unprecedented degree of distinction to handle 30 students in each class. With AI many possibilities can be seen in the teaching and learning system considering interest and understanding of an individual, which will increase efficiency of the education system.

Artificial Intelligence (AI) and related technologies like Data Mining, Machine Learning or Neural Networks became very popular in recent years. Many IT companies today require graduated students to understand and be able to apply these technologies. Application potential of AI is not limited only to robotics, image processing or intelligent agents, but also in engineering areas like computer networking and communication. However, on most universities, networking courses focus mainly on transmission protocols, network services and hardware design only while AI, machine learning or neural networks are taught separately. This causes a gap that emerges between AI theory and engineering approach. Thus, teachers of engineering courses are challenged how to introduce their students to an application of AI in the engineering areas, e.g., electronics, communication, embedded systems, power grids, etc. This paper shows how selected AI techniques presently used in computer networks can be incorporated into networking curriculum and demonstrated to students which extends student competencies and prepares them better into future jobs. We also present two case studies where AI techniques are applied on networking data in order to solve typical engineering problems.

Artificial intelligence (AI) is a discipline interested in the processes and methods that allow a machine to perform tasks related to human intelligence. It offers many opportunities related to problem solving, quick decision-making, increasing efficiency and reducing costs. Because of its so various fields of application, artificial intelligence is at the heart of the new industrial revolution. Algeria aims to present its AI strategy by 2020. In this paper, we are interested in defining AI, its potential fields of application, and in particular, its influence in the customer journey and position of RFID (Radio-Frequency Identification) in the chain; application in the aviation sector and its relationship to the Internet of Things are also described through examples.

Artificial intelligence (AI) is a key technology that is utilized in autonomous systems. However, using AI in such systems introduces also several safety challenges, many of which are related to complex human-AI interactions. In general, these challenges relate to the changing roles of the people who interact with the increasingly autonomous systems in various ways. In this paper, we consider a set of practical safety challenges related to applying advanced AI to autonomous machine systems and present solutions from the perspective of human factors. We apply the novel concept of AI awareness (AIA) to discuss the challenges in detail, and based on this, provide suggestions and guidelines for mitigating the AI safety risks with autonomous systems. In addition, we briefly consider the system design process to identify the actions that should be taken to ensure AIA throughout the different systems engineering design phases. The theoretical research presented in this paper aims to provide the first steps towards considering AIA in autonomous systems and understanding the human factors perspective viewpoint on AI safety for autonomous systems.

Artificial Intelligence (AI) is increasingly adopted by organizations. In general, scholars agree that the adoption of AI will be associated with substantial changes in the workplace. Empirical evidence on the phenomenon remains scarce, however. In this article, we explore the adoption of AI in the context of knowledge work. Drawing on case study research in eight German organizations that have either implemented AI or are in the process of developing AI systems, we identify three pervasive changes that knowledge workers perceive: a shift from manual labor and repetitive tasks to tasks that involve reasoning and empathy, an emergence of new tasks and roles, and an emergence of new skill requirements. In addition, we identify three factors that are conducive to the development of AI systems in the context of knowledge work: leadership support, participative change management, and effective integration of domain knowledge. Theoretical and managerial implications are discussed.

Artificial intelligence (AI) agent created with Deep Q-Networks (DQN) can defeat human agents in video games. Despite its high performance, DQN often exhibits odd behaviors, which could be immersion-breaking against the purpose of creating game AI. Moreover, DQN is capable of reacting to the game environment much faster than humans, making itself invincible (thus not fun to play with) in certain types of games. On the other hand, supervised learning framework trains an AI agent using historical play data of human agents as training data. Supervised learning agent exhibits a more human-like behavior than reinforcement learning agents because of imitating training data. However, its performance is often no better than human agents. The ultimate purpose of AI agents is to entertain human players. A good performance and a humanlike behavior are important factors of the AI agents, and both of them should be achieved simultaneously. This study proposes frameworks combining reinforcement learning and supervised learning and we call then separated network model and shared network model. We evaluated their performances by the game scores and behaviors by Turing test. The experimental results demonstrate that the proposed frameworks develop an AI agent of better performance than human agent and natural behavior than reinforcement learning agents.

Artificial intelligence (AI) is a very disruptive technology. When adopted by a software system, AI influences and significantly changes its architecture due to its complexity, as well as due to a need to adjust the existing system to use AI (e.g., adopt accelerators). This is particularly critical in traditional embedded systems as they focus on a tight coupling of software and hardware. In this paper, we present results of a survey on how well companies in embedded software domain understand AI, how they perceive its possible benefits, and how they discuss the adoption of AI and its influence on their software system architecture. The goal of this survey is to evaluate architectural techniques that companies currently use when trying to assess the influence of adopting AI and to discuss the adequacy of these techniques for this task.

Artificial Intelligence (AI) and Software Engineering (SE) have traditionally been largely independent disciplines. A common interest in developing computer systems is an area of overlap but fundamental differences in goals and objectives has resulted in little actual interaction. Limited success with chronic problems in both disciplines has forced practitioners in both fields to look beyond their local horizons in the hope of discovering new sources of impetus for further progress within their individual areas of interest. Software engineers are looking towards AI for both potential increase in software ‘power’ that AI techniques seems to promise, and exploitation of complexity-reduction strategies that AI has, of necessity, pioneered. AI practitioners, struggling with fragile demonstrations, would dearly like to import from SE tools, techniques and ‘know-how’ for the production of robust, reliable, and maintainable software systems. In this paper, I shall survey the range and scope of this two-way interaction between AI and SE.

Artificial intelligence (AI) is expected to be more promising in the coming years, with, for example, notable gains in productivity, although there may be a significant impact on job reduction, which may jeopardize labor sustainability. Accordingly, there is a need to better understand this phenomenon and to analyze it in the light of a particular theory. However, there is a scarcity of AI theories in the service management literature. In order to obtain a better understanding of the subject, we have conducted a systematic review of the literature to provide a comprehensive analysis of the theories developed regarding AI in service management. The results have showed a wide range of theories, but not all directly related with AI; the latter are smaller in number making it difficult to draw a clear pattern. At current days, researchers are slowly advancing with new AI theories and moving away from those already in use, such as in computer science, ethics, philosophical theories, and so on.