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The claim that super intelligent machines constitute a major existential risk was recently defended in Nick Bostrom’s book Superintelligence and forms the basis of the sub-discipline AI risk. The purpose of this paper is to critically assess the philosophical assumptions that are of importance to the argument that AI could pose an existential risk and if so, the character of that risk.

This paper distinguishes between “intelligence” or the cognitive capacity of an individual and “techne”, a more general ability to solve problems using, for example, technological artifacts. While human intelligence has not changed much over historical time, human techne has improved considerably. Moreover, the fact that human techne has more variance across individuals than human intelligence suggests that if machine techne were to surpass human techne, the transition is likely going to be prolonged rather than explosive.

Some constraints for the intelligence explosion scenario are presented that imply that AI could be controlled by human organizations.

If true, this argument suggests that efforts should focus on devising strategies to control AI rather strategies that assume that such control is impossible.

This paper aims to inform policymakers about key artificial intelligence (AI) technologies, risks and trends in national AI strategies. It suggests a framework of social governance to ensure emergence of safe and beneficial AI.

The paper is based on approximately 100 interviews with researchers, executives of traditional companies and startups and policymakers in seven countries. The interviews were carried out in January-August 2017.

Policymakers still need to develop an informed, scientifically grounded and forward-looking view on what societies and businesses might expect from AI. There is lack of transparency on what key AI risks are and what might be regulatory approaches to handle them. There is no collaborative framework in place involving all important actors to decide on AI technology design principles and governance. Today's technology decisions will have long-term consequences on lives of billions of people and competitiveness of millions of businesses.

The research did not include a lot of insights from the emerging markets.

Policymakers will understand the scope of most important AI concepts, risks and national strategies.

AI is progressing at a very fast rate, changing industries, businesses and approaches how companies learn, generate business insights, design products and communicate with their employees and customers. It has a big societal impact, as – if not designed with care – it can scale human bias, increase cybersecurity risk and lead to negative shifts in employment. Like no other invention, it can tighten control by the few over the many, spread false information and propaganda and therewith shape the perception of people, communities and enterprises.

This paper is a compendium on the most important concepts of AI, bringing clarity into discussions around AI risks and the ways to mitigate them. The breadth of topics is valuable to policymakers, students, practitioners, general executives and board directors alike.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.

The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.

The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.

The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.

The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.

Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.

At the doorsteps of the fourth wave of the industrial revolution, it is compulsory to develop a new understanding regarding the future of human labor based on “Industry 4.0” for German manufacturers, and two American concepts: “The Industrial Internet” and “The Internet of Things.” How will the nature of human work be in the digital economy of the forthcoming future? The problem of unemployment and the composition of the labor market, in terms of professional skills, are yet to be waiting for answers. Scientific management is also transforming to answer the emerging requirements of this new era, as “Digital Taylorism” to re-organize work in a techno-centric manner. Accordingly, the aim of this chapter is to examine the nature and the possible opportunities and threats of the digital age and try to develop a digital Taylorism understanding to minimize the negative impacts of digitalism on both individual workers and society in a way that all parts including the manufacturers can fully take the benefit of potential advantages of this new era.

Algorithms, artificial intelligence (AI), machines, and all emerging digital technologies disrupt traditional auditing, raising many questions and debates. One of the central issues of this debate is the human-algorithms complex duality, which focuses on this investigation. This study aims to investigate the algorithms’ penetration in auditing activities, with a specific focus of a future scenario on the human-algorithms interaction in performing audits as intelligent teams.

The research uses a qualitative reflexive thematic analysis, taking into consideration the academic literature, as well as professional reports and websites of the “Big Four” audit firms and internationally recognized accounting bodies.

The results debate the complex duality between algorithms and human-based actions in the institutional settings of auditing activities by highlighting the actual stage of algorithms, machines and AI emergence in audit and providing real-life examples of their use in the audit. Furthermore, they emphasize the strengths and weaknesses of algorithms compared to human beings. Based on the results, a discussion on the human-algorithms interaction from the lens of the Human-in-the-Loop (HITL) approach concludes that the Auditor-Governing-the-Loop may be a possible scenario for the future of the auditing profession.

This study is exploratory, investigating academia and practitioners’ written debates, analyzes and reports, limiting its applicability. Nonetheless, the paper adds to the ongoing discussion on emerging technologies and auditing research. Finally, the authors address some potential biases associated with the extended use of algorithms and discuss future research implications. Future research should empirically test how the human-algorithms tandem is working and how AI and other emerging technologies will affect auditing activities and the auditing profession.

The study provides valuable insights for audit firms, auditors, professional organizations and standard-setters, and regulators revealing the implication of algorithms’ penetration in auditing activities from the human-algorithms complex duality perspective. Moreover, the academic education and research implications are highlighted, in terms of updating the educational curriculum by including the new technologies issues, as well as the need for further research investigations concerning the human-algorithms interactions issues as, for example, trust, legal restrictions, ethical concerns, security and responsibility.

The research uses HITL as a novel paradigm for responsible AI development in auditing. The study points to the strategic value of a HITL pattern for organizational reflexivity that, according to the study, ensures that the algorithm’s output meets the audit organization’s requirements and changes in the environment.

Artificial intelligence (AI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. It is also known as machine intelligence (MI) and is used because a machine mimics the cognitive functions that humans associate with human ability, such as logical reasoning, learning, and problem-solving. From Facebook’s automatic tagging suggestions to driverless cars, AI is rapidly progressing, and therefore, the ethical and moral question now is not whether AI should exist or not. AI exists and is already helping in improving various aspects of life such as health, safety, convenience, and overall standard of living. AI can replace or substitute routine mechanical, repetitive, boring jobs to free and unleash human creative and innovative talent to big thinking projects and humanizing work and society. AI can provide digital assistance in routine day-to-day tasks, detect cancer, diagnose rare diseases, and even prevent car crashes. AI can replace jobs, however, but not human work. Work as a duty, self-actualization and destiny will always continue, if not on the shop or office floors or boardrooms, at home, gardens, places of prayer and worship, and labs of creativity and innovation, in society and civilizations. While AI may indirectly free human talent for more meaningful and creative work, it can rarely participate in higher purposes such as creating bonding and belonging groups, in creating forgiving and compassionate communities, in drumming up small business, startups and corporations, and in harmonizing and humanizing this planet and cosmos for bliss or happiness. This chapter on AI, while investigating its market turbulence, will go beyond the legal aspects to ethical, moral, and spiritual dimensions and sacred opportunities of AI.

A strictly pragmatic stance is taken in asking the question “What features must be present that makes behaviour intelligent?” The Turing Test is shown to be insufficient to support any useful discussion; intelligence measures (IQ tests) suggest specialisation and little else. On the other hand, Discontinuity Theory identifies “insight” and Information Theory provides a means of measuring the practical consequence of “insight” as well as providing an argument for the need of “purpose” in intelligent behaviour. The Peircian trichotomy of inference into Induction, Deduction and Abduction supports a range of specialisation for the different aspects of reasoning. These aspects can be improved through experience leading to the notion of “wisdom” and a practical measure for the anthropomorphism of intelligence. The simplest kind of intelligence is constructed as a computer program demonstrating that intelligent machines as they are currently conceived are unlikely to be independent of their human context.