The consistent development of information technology (IT) paves the way for companies to make the shift to digital work as their principal mode of operation. This is made feasible by the rapid progress of IT. As a consequence of this, employers are putting pressure on employees to adapt to new forms of employment, which may include less interaction with other people but more interaction with information technology. However, as a consequence of these new ways of doing things, workers won't be able to carry out their responsibilities with the same principles and beliefs that they have been used to bringing to the table in the past. The continual upheaval that takes place in the workplace has the potential to influence the self-beliefs that constitute a person's professional identity at work, also known as the perception of one's function in the workplace. This is because self beliefs are sensitive to being influenced by the perception of one's function in the workplace. The act of having one's identity questioned by an experience that is in direct opposition to who they are may result in a decline in one's sense of self-worth as well as a potential threat to the integrity of one's identity. As a consequence of this, it is possible that activities that are targeted at maintaining self-esteem connected with identity will be necessary in light of the fact that the landscape and experiences of a number of professions have been transformed as a result of the development of technology. The digitization of workplaces is directly responsible for the growing popularity of digital labour as the normal operating procedure in organisations. One of the primary factors that is driving this discussion is the continuing development of artificial intelligence (AI), which can be defined as "the ability of a machine to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, decision-making, and even demonstrating creativity." Artificial intelligence is put to use in many different capacities within the field of digital labour, including (managerial) decision making, data analysis and prediction work, or (human-AI) interaction. 1 | P a ge Because of this, artificial intelligence will continually bring about changes to working environments and professions, perhaps putting the lives of people whose jobs are replaced by computers in jeopardy. On the other hand, this might lead to a reduction in value if the people who utilise AI systems have major variances in their perspectives. In addition, the use of AI has the potential to contribute to the growth of ambiguity and the invasion of individuals' right to personal privacy. The phrase "dark side of AI" is often used to refer to this undesirable phenomenon, which outlines the ways in which AI offers risks for individuals, businesses, and society as a whole. However, the adoption of AI in enterprises may not only eliminate or modify current jobs but also create new sectors of labour, such as in the disciplines of engineering, programming, or even social domains. This is because AI may be able to perform some or all of the tasks associated with these vocations. This is due to the fact that AI is capable of learning new things and adjusting to its surroundings. There is an ongoing sense of optimism over artificial intelligence and the economic effects that it will have (Selz, 2020). The public discourse about artificial intelligence has been more optimistic over the last several years; despite this, the concern that AI would displace current jobs continues to outweigh the potential for human and AI collaboration in the future. The interaction between humans and artificial intelligence demonstrates that people's views of AI are based on a wide variety of features to varying degrees. For example, salient signals, affordances, or collaborative interaction may have an effect on a person's emotions and, as a consequence, their intents about artificial intelligence (Shin, 2021). The manner in which an employee applies technology in the course of their work contributes to the formation of that employee's sense of self identity. In order to investigate this matter in a way that is adequate, we are going to adopt the perspective of Carter and who define the word "IT identity" as "the extent to which a person views use of an IT as integral to his or her sense of self." This will allow us to investigate this matter in a manner that is adequate. It is possible that the implementation of AI in the workplace will run opposite to the employees' identification with their activities, which may cause them to engage in resistive behaviours such as an aversion to algorithms on their part. The phenomenon known as "algorithm aversion" is characterised by the fact that employees, when faced with the same conditions as before, prefer to get assistance from a human being rather than from a computer programme. A possible definition of IT identity danger is "the anticipation of harm to an individual's self-beliefs, caused 2 | P a ge by the use of an IT, and the entity it applies to is the individual user of an IT." The individual user of an IT is the entity to whom this definition applies.A term that might be used to describe this obstruction is "IT identity threat." As a consequence of this, having an awareness of the development of upcoming predictors that impact AI resistance based on IT identity risks is very necessary. This is owing to the fact that it is anticipated that the introduction of AI would modify employment inside enterprises, which in turn may have an influence on the identities of the individuals working in such firms.

Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]

Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

"The rise of AI must be better managed in the near term in order to mitigate longer term risks and to ensure that AI does not reinforce existing inequalities"--Publisher.