Includes bibliographical references and index

(p.2) The fear of technological unemployment has been accompanying the great innovative waves. However, in the history of humanity, periods of intensive automation have often coincided with the emergence of new jobs, tasks, activities and industries. Indeed, the challenging question is related to the overall sign of the relationship between technological change and labor: is technology labor-friendly or is it labor-threatening?

(p.32) The other impact of AI in higher education is enrollment. Liberal arts and humanities majors may become more popular as these areas are less susceptible to “AI-invasion.”

(p.6) As time progresses, artificial intelligent systems should become better and more flexible by incorporating changes in the academic culture and learning preferences of new generations of students (Baker, 2016). Robots are developing their human touch too. Robots that can recognize emotional states are currently in development (Azuar et al., 2019; Yu and Tapus, 2019). Only time will tell what robots with deep learning ability will be capable of, and whether they will be artificially intelligent and we anatomists remain naturally stupid. It is too early to say that anatomy educators are safe from losing their jobs to robots. However, this...

(p.485) Machine learning (ML), in combination with complementary technologies such as robotics and software-based standardization, have rapidly become real substitutes and complements to human labor. [...] While recent advances in technology seem able to automate intelligent work, we lack good data on the scope of such automation.

(p.5) New, AI-based digital technologies may allow larger segments of the labor market to improve their productivity and to access better paying occupations and, thereby, may help promote (inclusive) growth. This requires, however, that a certain number of policies are put in place that support the necessary shift in occupational demand, maintain a strong competitive environment to guarantee diffusion of innovation, and keep up aggregate demand to support structural transformation. At the same time, AI applications raise the potential for productivity growth for interpersonal, less technical occupations and tasks, leading to higher demand for such work, which is likely to dampen...

(p.6535) Recent studies show that historical technology-driven trends may not capture the AI-driven trends we face today. Consequently, some have concluded that AI is a fundamentally new technology (3, 65). If the trends of the past are not predictive of the employment trends from current or future technologies, then how can policy makers maintain and create new employment opportunities in the face of AI? What features of a labor market lead to generalized labor resilience to technological change?

(p.6536) The impact of AI and automation will vary greatly across geography, which has implications for the labor force, urban–rural discrepancies, and changes in the income distribution. The study of AI and automation are largely focused on national employment trends and national wealth disparity. However, recent work demonstrates that some places (e.g., cities) are more susceptible to technological change than others. Occupations form a network of dependencies which constrain how easily jobs can be replaced by technology. Therefore, the health of the aggregate labor market may depend on the impact of technology on specific urban and rural labor markets.

(p.163) One particular concern with AI is that the changes will happen so quickly that there will be sustained periods of time in which large segments of the population are not working (see Goolsbee [forthcoming] for a discussion of speed of adoption and Acemoglu and Restrepo [2016] for a useful model). These rapid changes, and the potential disruption to the workforce, suggest it is important that there are policies in place to support workers and retraining.

(p.87) The problem is that we have a hard time knowing what lies ahead in this new frontier. Who would have thought a few decades ago that most people today would do their work by staring into PC monitors, laptops, and mobile devices? There is no fixed amount of human endeavor, and work of different kinds will always appear to fill new economic demands.

(p.89) Some new jobs may appear, but they will not last for long. Machines have begun to learn by observation, by trial and error, and even from other machines—as we do, but much faster. They are likely to master most new occupations before we humans ever have the chance. We face a time when humans will hop from one career to the next, struggling to stay ahead of automation. Saddled by debt and discouraged by a broken social contract, many may succumb to despair unless we find an alternative to endless retraining.

(p.414) It is possible that artificial general intelligence—AI that out-performs humans in all ways—will arrive in several decades and the resulting employment disruptions will dwarf the disruptions described in this paper. But between today and 2040, the AI that already exists will disrupt the nation’s occupational structure. The question is whether these disruptions will seriously destabilize the country’s political and social structure. Part of the answer involves the speed of technical disruptions. The evidence in this article is mixed, but on balance there appears to be some time to develop anticipatory policies to assist people who will lose jobs and other...

(p.185) Another key theme gleaned from the analysis was the psychological impact of technology adoption, particularly AI as it can be at the expense of people. A manager provides some further insight into the psychological elements management encounter: ‘They amass experience which gives them the edge, ability of a TM to look at a warehouse full of pallets to say, I need 15 people and 3 h to shift that… that is purely experience. We put a system in and a report can tell you that. That’s a massive hit for someone. That first barrier is biggest’. If AI and automation...

(p.10) Artificial intelligence has the power to change the nature of work for many people, but the pace of adoption and the extent of the disruption are still the subject of debate. A rapid adoption of autonomous self-driving vehicles, for instance, could dramatically displace millions of workers, but a more gradual and partial adoption, especially in a growing economy, will have far less impact on drivers.

(p.584) Developmental disability is a significant, chronic disability that is attributed to physical, mental or a combination of mental and physical impairments. According to [2] these functional limitations in life activities include learning, mobility, self-direction, language reception and expression, and capacity for independent living. Instead of collaborative robots, or co-robots, completely displacing workers with IDD, social co-robots may have promise for not just augmenting the physical and intellectual capabilities for these workers but improving the attitudes and enjoyment for them in their employment environment.

(p.20) Because of rapid technological development, many commentators point to inevitable technological changes in the future of work. However, the overview that emerges from this review of the material suggests that multiple possible futures exist that depend on complex dynamics between context, choices and adaptability to new circumstances shaping the opportunities for individuals, firms, civil society organisations, governments and international organisations.

(p.197) The expert replacement model applies a Taylorist industrial model of mental labor. Knowledge is viewed as a scarce resource to be "extracted" from experts and embodied in computer systems, which can then be operated more profitably.

(p.1) Artificial intelligence (AI) and its relation to work have become central in our cultural discourse, clear to even a casual reader of contemporary news and media outlets. Technological breakthroughs in the field of AI promise to change the way we organize work (Davenport & Kirby, 2016). The artful integration of AI and work, however, remains an open challenge. There is currently limited empirical understanding and research to guide the information community in this area—for example, labor, motivation, cognition, machine learning, data science, human-computer interaction, and information science, among others—in coherent ways. Such...

(p.3) Exploring AI futures and affordances entails an understanding of not only AI in the world of work but equally important, the world of work in AI. The world of work sneaks into AI in the form of big data feeding the algorithms, data management activities, the bias of past practices, ethics, and governance.