Recent years have yielded significant advances in computing and communication technologies, with profound impacts on society. Technology is transforming the way we work, play, and interact with others. From these technological capabilities, new industries, organizational forms, and business models are emerging.
Technological advances can create enormous economic and other benefits, but can also lead to significant changes for workers. IT and automation can change the way work is conducted, by augmenting or replacing workers in specific tasks. This can shift the demand for some types of human labor, eliminating some jobs and creating new ones.
Advances in fields such as artificial intelligence and robotics are making it increasingly possible for machines to perform not only physical but also cognitive tasks currently performed by humans. These developments have led to widespread interest in the future of work.
This report explores the interactions between technological, economic, and societal trends and identifies possible near-term developments for work. It emphasizes the need to understand and track these trends and develop strategies to inform, prepare for, and respond to changes in the labor market. It offers evaluations of what is known, notes open questions to be addressed, and identifies promising research pathways moving forward.
THE CHANGING TECHNOLOGY LANDSCAPE
Information technologies have already transformed society, and more changes are inevitable. Computing power and network speed have grown dramatically. Access to the Internet has grown in the United States and worldwide. Organizations are increasingly moving their core business processes—such as accounting, sales, and material resource planning—online. Videoconferencing is increasingly used throughout organizations to enable the geographical distribution of project work via meetings that integrate computer presentations, face-to-face exchanges, and data sharing. Peer-to-peer networks have emerged to connect resource holders with resource seekers, leading to companies such as eBay, Uber, and Airbnb, and new online reputation systems facilitate feedback reporting for both providers and customers. Related IT tools have also been steadily augmenting traditional tools for education and training, leading to the emergence of the phenomenon of massive open online courses (MOOCs) and other educational innovations.
At the same time, computers have become increasingly competent at both physical and cognitive tasks that have previously been done primarily by humans, such as speech recognition, identifying faces and other objects in images, interpreting text, analyzing medical data, driving cars, and many other tasks. Much of this progress is due to advances in artificial intelligence (AI)—software-based systems that aim to mimic aspects of human intelligence. Over the past decade, a number of highly visible AI systems have emerged in a range of fields, from mobile devices to cars with autopilot functions. AI has defeated human champions at games such as chess and Go, and AI systems have been developed that are capable of answering a growing range of factual questions and serving as intelligent software agents. Automated software-based agents, such as chatbots that answer simple queries and hold conversations with humans and bots that conduct activities like automated financial trading, are also emerging.
Recent advances in AI have been driven largely by advances in machine learning—algorithms that improve through experience, often by identifying patterns from historical data that may be extrapolated to future purposes. For example, such techniques have been used to predict patient responses to medical treatment based on historical medical records and to process human (or “natural”) language in useful ways. A particular set of algorithms, called deep neural networks, have been a driver of recent advances in areas such as computer vision, speech recognition, and text analysis. The increasing generation of online data is expected to further fuel the development of these machine learning systems. Advances in robotics have led to increased factory automation
and to initial demonstrations of autonomous vehicles on land, sea, and air. Technologies for service and companion robots are in their infancy.
Humans are still more effective than computers at many tasks, especially those that require creative reasoning, nonroutine dexterity, and interpersonal empathy. New models of human engagement have focused on how best to combine the strengths of humans and computers to complete a given task, referred to variously as complementary computing, mixed-initiative interaction, or collective intelligence. The field of human-centered automation focuses on enhancing situational awareness of human operators, developing common operating pictures across multiple users, and building predictive models of human behavior in different contexts.
On balance, the rapid pace of technological advances is likely to continue in frontier areas, where investments in research and development are increasing. Computer performance continues to improve via advances in hardware parallelism, hardware specialization, and enhanced programming languages. Beyond speedup, a broad range of progress has been seen in important technologies such as the mobile Internet, the Internet of Things, cloud computing and storage, AI, robotics, virtual and augmented reality, and machine learning. Research continues in more speculative potential breakthrough areas like bionics. Significant progress in any one of these technologies would likely have profound effects on the workforce.
Opportunities for digitizing and automating tasks are far from exhausted. In particular, the workforce will be increasingly affected as more and more cognitive tasks become fully or partly automatable—from language processing to problem solving and pattern matching—and as advances in robotics yield enhanced physical dexterity, mobility, and sensory perception in machines. These trends will almost surely change the demand for the workers performing these tasks and the nature of the organizations in which they work.
Robotic automation will continue to advance, in assembly lines and other workplaces and in areas that have not yet been touched significantly by robotic technologies. Over the next decade, self-driving vehicles, already in limited trial or commercial use (e.g., from Google/Waymo, Tesla, nuTonomy, Uber, and many others), will mature and become more widespread, with potentially significant impacts on employment in the transportation sector, ultimately reducing the need for human taxi drivers and long-haul truckers. Computer competence in perceptual tasks, including speech recognition and computer vision, will also advance, likely leading to superhuman competencies for listening and image processing by computer. This could affect jobs involving pattern recognition, including those of pathologists, radiologists, and security workers.
Automatic translation between languages by computers, already in use, though imperfect, will probably improve to the point of routine use of real-time translating telephones and earpieces. The ability of computers to interpret and extract information from unstructured text will continue to advance, with potentially significant effects on automating knowledge-worker jobs, such as paralegal research.
EFFECTS OF INFORMATION TECHNOLOGY ON PRODUCTIVITY AND INEQUALITY
Because computerization changes the cost structures of processes, goods, and services, the increasing adoption of IT is transforming the economics of many industries and functions. Productivity growth, the predominant contributor to increases in standards of living, rose rapidly from the late 1990s to the early 2000s, in part reflecting advances in IT. However, productivity growth has slowed during the past 10 years, according to official data from U.S. statistical agencies. Some of this slowdown is accounted for by less rapid improvements in the IT-producing and the IT-using sectors of the economy.1 However, these statistics are difficult to interpret, partly due to output and input price deflators that cannot fully account for changes in quality as well as the proliferation of free digital goods and services. There is evidence that the diffusion and successful adoption of IT advances is time- and resource-intensive, producing a lag, possibly measured in years or even decades, between technological advances and resulting productivity growth.2 Emerging evidence suggests that this diffusion is increasingly uneven, leading to bigger productivity gaps between frontier firms and those in the middle of the distribution.3
Income and wealth inequality has increased over the past 20 years in the United States, with median family income stagnating while incomes rose significantly for the top 1 percent; significant disparities also exist among the other 99 percent, largely correlated to a rising premium of education. The share of wealth owned by the bottom 80 percent has fallen
1 J.G. Fernald, 2015, Productivity and potential output before, during, and after the Great Recession, NBER Macroeconomics Annual 2014, doi: 10.3386/w20248.
2 See, for example, P.A. David, 1990, The dynamo and the computer: An historical perspective on the modern productivity paradox, American Economic Review 80.2:355-361; and E. Brynjolfsson and L.M. Hitt, 2003, Computing productivity: Firm-level evidence, Review of Economics and Statistics 85.4:793-808.
3 D. Andrews, C. Criscuolo, and P.N. Gal, 2015, “Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries, OECD Publishing, http://www.oecd.org/eco/growth/Frontier-Firms-Technology-Diffusion-and-Public-Policy-Micro-Evidencefrom-OECD-Countries.pdf.
from 18.7 percent in 1983 to 11.1 percent in 2010.4 The mix of jobs in the economy continues to change, as many routine information-processing tasks are being automated in whole or in part, even as the numbers of low-wage service jobs and high-skill professional jobs have grown. There is also evidence that, since 2000, social skills have been increasingly valued in the labor market.5
It has been predicted that the future effects of IT on the workforce are likely to be larger than those we have already seen, especially as AI-based and robotics systems improve.6 However, it is not known whether new technologies will automate and replace workers in existing tasks more rapidly than the economy as a whole (driven by various factors, including automation) creates new demands for labor. The net effect is difficult to predict; it is easier to anticipate how new technologies will automate existing tasks than it is to imagine tasks that do not yet exist and how new technologies may stimulate greater consumer demand. Furthermore, the future of employment is not only a question of the availability of tasks to be performed, but how they are organized and compensated. In addition, digital goods have the potential to diffuse rapidly because they are infinitely replicable via shared digital platforms. However, implementation and customization of software can take a surprisingly long time, as can necessary changes to complementary skills, organizations, and institutions. Future innovations could have more immediate impact if organizations become more able to incorporate them quickly. These are matters of business strategy, social organization, economic policies and programs, and political choices and are not simply driven by technology alone.
CHANGES IN THE NATURE OF WORK AND ITS ORGANIZATION
Business organization is also in the midst of a transformation. Not only is the traditional employment model changing, but nontraditional models are increasingly facilitated by technology. For traditional firms, despite a burst of new start-up companies in many areas, statistics suggest
4 For this and other statistics on wealth inequality, see E.N. Wolff, 2012, The Asset Price Meltdown and the Wealth of the Middle Class, New York University, New York; A.B. Atkinson, T. Piketty, and E. Saez, 2011, Top incomes in the long run of history, Journal of Economic Literature 49.1:3-71; and T. Piketty, 2014, Capital in the Twenty-First Century, Harvard University, Cambridge, Mass.
5 See, for example, D.J. Deming, 2015, “The Growing Importance of Social Skills in the Labor Market,” National Bureau of Economic Research, doi: 10.3386/w21473.
6 M. Chui, J. Manyika, and M. Miremadi, 2016, “Where Machines Could Replace Humans—And Where They Can’t (Yet),” McKinsey Quarterly, http://www.mckinsey.com/business-functions/business-technology/our-insights/Where-machines-could-replacehumans-and-where-they-cant-yet; E. Brynjolfsson and A. McAfee, 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, WW Norton & Company.
there are fewer new, growing companies in the United States today than in the past, and this category now employs a smaller share of the workforce. Data from recent decades also show a drop in the pace of job and worker dynamism and reallocation7—especially since 2000. At the same time, nontraditional types of employment—other than the 40-hour-per-week job at a single company offering health and retirement benefits—appear to be increasing. While nontraditional work as independent contractors and temporary agency employees has been growing for decades, IT advances now make it easier to access such employment opportunities, and in some cases to perform work remotely over the Internet. This has given rise to new companies based on technology-mediated “on-demand” or “gig” employment at both the low-skill (e.g., TaskRabbit) and high-skill (e.g., Upwork) ends of the spectrum. For now, the technology-enabled examples of on-demand work are a small fraction of overall employment: recent research suggests that less than 1 percent of the U.S. workforce currently uses online platforms for temporary or gig work.8
IT is playing a growing role in many organizations, including greater electronic record keeping, communications, and automation of work flows, although most organizations and markets are far from fully digital. Organizations are now relying increasingly on virtual teams of workers, teams whose members primarily interact via digital technologies across diverse geographies. Increased availability of digital data has facilitated a tripling in the use of “data-driven decision making” between 2005 and 2010.9 Privacy, security, and data ownership have become increasing concerns as more and more information, including personal data, has been digitized and networked. The number of nonemployer businesses (businesses with no workers or only independent contractors) appears to be growing.
Data on many of these trends are elusive, reflecting both the rapidly changing nature of society and the economy and gaps in national and private data collection and statistical infrastructure. While improvements in and diffusion of IT have had profound effects on many aspects of the workforce, the future effects of these advances on the workforce and the broader economy are difficult to predict. This partly reflects our inadequate understanding of the complex interactions among technologies themselves combined with the skills, organizations, institutions, policies, and human preferences in society.
7 Measured either by total job creation plus job destruction, or by total hires plus separations, or by geographic mobility of workers.
8 L.F. Katz and A.B. Krueger, 2016, “The Rise and Nature of Alternative Work Arrangements in the United States, 1995-2015.”
9 E. Brynjolfsson and K. McElheren, 2016, The rapid adoption of data-driven decision making, American Economic Review 106(5):133-139.
Education remains a key influence on worker income. Wage disparities between non-college-educated workers, college-educated workers, and workers with graduate degrees, which grew rapidly in the 1980s and 1990s, have leveled off but remain high in this century. There are also disparities in job stability and benefits between these groups.
New uses of IT in teaching, including online courses,10 are increasingly available and hold the potential to expand access to education. New companies are specializing in just-in-time training targeted to specific companies and employment opportunities,11 but the ultimate impact of these tools remains to be seen.
DATA AND METHODS FOR EVALUATING TECHNOLOGY AND WORKFORCE TRENDS
Traditionally, data such as employment numbers and salaries collected by federal statistical agencies have been invaluable for understanding the status of the workforce and the economy at large, and for tracking technology-related measures such as productivity.12 These surveys are often time- and resource-intensive to complete and must be updated periodically.
At the same time, the ubiquity of digital transactions is producing increasing amounts of born-digital data of potential use for tracking and understanding technology-related workforce trends. Traditional survey data are increasingly being augmented by or integrated with administrative data (collected in the course of routine transactions), resulting in new statistical products and integration of firm-level information with information at the individual worker level. There is also great potential to use online data about worker profiles and job listings to understand worker skills, demand for employees, occupational skills requirements, and related information. New ways to integrate various data sources while also protecting privacy and confidential business information could reveal valuable information about the changing workforce.
10 Examples are edX and Coursera, which are both websites that offers free online courses and classes from the world’s best universities.
11 An example is Udacity, a for-profit education organization, which offers nanodegrees and credentials in areas such as web development and data analysis (among others). Udacity was born from a Stanford University experiment where Sebastian Thurn and Peter Norvig offered their “Introduction to Artificial Intelligence” course for free online. See Udacity, 2016, “About Us,” Udacity, https://www.udacity.com/us, accessed May 2016.
12 Such sources include data from the Current Establishment Survey, the Quarterly Census of Employment and Wages, the Current Population Survey, the Decennial Census, the American Community Survey, the Job Openings and Labor Turnover Survey, and the Business Employment Dynamics and Business Dynamic Statistics data.
While quantitative information, including analytical methods using very large data sets, can be useful for understanding the labor market and other workforce trends, qualitative and microdata and methods help to elucidate the correct research questions and to understand causality. Such methods, including case studies, participant observation, ethnographic interviewing, life histories, and the textual analysis of data are important for informing macro-level research.
Finally, there is significant interest in predicting which jobs are most likely to be automated (and to what extent), especially due to advances in AI, machine learning, and robotics. Several recent studies have aimed to quantify probabilities of automation by comparing specific technology capabilities to the skills required for tasks associated with specific jobs. While results suggest that automation of a large number of jobs will become increasingly technically feasible, component tasks are more easily automated than entire occupations. Research also suggests that lower-wage jobs may be more susceptible to partial or full automation.
Moving forward, policy makers and the research community would be well served by data collection designed to support longitudinal tracking and analysis of workforce trends and the role of advances in IT.
Six general findings emerge from this study.
- Advances in IT are far from over, and some of the biggest improvements in areas like AI are likely still to come. Improvements are expected in some areas and entirely new capabilities may emerge in others.
- These advances in technology will result in automation of some jobs, augmentation of workers’ abilities to perform others, and the creation of still others. The ultimate effects of information technology are determined not just by technical capabilities, but also by how the technology is used and how individuals, organizations, and policy makers prepare for or respond to associated shifts in the economic or social landscape.
- The recent increase in income inequality in the United States is due to multiple forces, including advances in IT and its diffusion, globalization, and economic policy.
- IT is enabling new work relationships, including a new form of on-demand employment. Although current digital platforms for on-demand work directly involve less than 1 percent of the workforce, they display significant growth potential.
- As IT continues to complement or substitute for many work tasks, workers will require skills that increasingly emphasize creativity, adapt-
ability, and interpersonal skills over routine information processing and manual tasks. The education system will need to adapt to prepare individuals for the changing labor market. At the same time, recent IT advances offer new and potentially more widely accessible ways to access education.
- Policy makers and researchers would benefit significantly from a better understanding of evolving IT options and their implications for the workforce. In particular, (1) sustained, integrated, multidisciplinary research and (2) improved, ongoing tracking of workforce and technology developments would be of great value for informing public policies, organizational choices, and education and training strategies.
A RESEARCH AGENDA
Federal agencies or other organizations that sponsor research or collect data relevant to technology and the workforce should establish a sustained, multidisciplinary research program in order to address the many important yet unanswered questions about how technology is changing, might change, or could help to shape the nature of work and the U.S. national economy. This will help to expand a knowledge base that will ultimately help a variety of stakeholders address productivity growth, job creation, and the transformation of work, and feed directly into the National Science Foundation’s new interest in research on work at the human-technology frontier.13 The program should
- Target the understanding of how technology choices can affect the workforce to improve the design of policies and technologies that will benefit workers, the economy, and society at large;
- Emphasize feedback between micro- and macro-level research methods and among the social sciences, economics, computer and information sciences, and engineering; and
- Establish and facilitate the use of new data sources, tools, methods, and infrastructure to support such research while protecting privacy, including increased use of data sources developed in the private sector.
Such a research program should span a range of themes, such as those described below.
13 National Science Foundation, 2016, “10 Big Ideas for Future NSF Investments,” https://www.nsf.gov/about/congress/reports/nsf_big_ideas.pdf, accessed December 2016.
Theme 1: Evaluating and Tracking Progress in IT
Research to develop new ways of evaluating and tracking progress in IT would help decision makers understand impacts of technology on the workforce and inform strategies to help prepare for imminent changes.
Such research could focus on the following objectives:
- Develop, refine, and test improved strategies for classifying technological capabilities in terms of the human skills and tasks they can or could replace.
- Identify key indicators that could signal the extent of the impact of developments in a given technological field.
- Develop new mechanisms to track and forecast technological and economic changes of particular relevance to the future of the workforce.
- Develop indexes, analogous to the Consumer Price Index, to assess (1) the current state of technologies, (2) the degree of diffusion of technologies into firms and organizations, and (3) the technological capabilities and diffusion of AI and robotics, in particular.
Theme 2: Technology Adoption and Impact Within Organizations
IT can have a significant impact on the type and nature of tasks performed by workers, depending on the specific content of the task. New research could be pursued to elucidate ways in which different industries use technology to organize their operations, allocate tasks, and perform specific functions. Such work could be conducted at both the micro- and macro-level scales to provide a firm- and industry-level window into the impacts of technology on employees in a given industry or at a given organizational level.
Theme 3: Impacts of Policy Choices
Research on impacts of public policy choices could identify policies, resources, and practices that would mitigate technological unemployment, approaches to easing transitions for workers forced to change occupational fields due to technological change, and opportunities for actively guiding the future impacts of technology development and deployment before they occur.
Theme 4: Working with Emerging Technologies
As emerging technologies diffuse into different industries, individuals must learn how to interact with these technologies to successfully com-
plete tasks, which can affect the nature of decision making, teamwork, and organization. Some teams use technology as a means for connecting or convening, or in place of some aspect of human intelligence. The rise of data-driven decision making14 and new forms of collective intelligence reflect the ways that technology and humans can work together to act more intelligently than they could separately. Research is needed to understand technology-augmented organizations, teams, and individuals and the conditions under which they are most effective.
Theme 5: Societal Acceptance of Automation Technologies
The mere existence of a technology does not guarantee that it will be deployed. Economic costs and benefits influence decisions to deploy technologies, as do many other factors. In some contexts, people (either workers or customers) may prefer to interact with a human over a machine (or vice versa). This may reflect the existence of important, yet largely invisible and unremunerated, human skills that can easily be missed in existing skill categories and national statistics. Consumer behaviors and worker preferences and bargaining power will drive markets; understanding the behavioral economics of automation will be important for understanding its adoption patterns. Additional human factors and the social, philosophical, and psychological dynamics of automation could be explored.
Theme 6: Changing Labor and Skill Demands and Implications for Education and Training
Changes in technology use affect the roles of workers and contribute to changing labor and skills demands. This creates challenges for individuals planning their career strategies and for employers, educational institutions, and policy makers. Research tracking and mapping the changing labor and skills demands in specific industries and occupational fields over time, along with regional variations and associated policy implications, could provide insights into such trends. This research could evaluate the extent of IT diffusion into different occupational fields. Researchers could develop and test hypotheses about how these technologies change the work functions, tasks, skills requirements, and demand for these fields. The economic insecurity felt by many workers under-
14 See, for example, E. Brynjolfsson and K. McElheran, 2016, Digitization and innovation: The rapid adoption of data-driven decision-making, American Economic Review 106.5:133-139.
scores the importance of understanding the interplay of technology with jobs, wages, and opportunity.15
Furthermore, the new workplace requires a workforce trained for expertise in areas that will drive the future economy and with the flexibility to adapt to rapid change. Because education will significantly determine the success of the United States in responding to the changing workplace, a better understanding of effective strategies is critical. While the United States has a poor track record of predicting future workforce skills demands, some insight can be gained from how skill demands are currently changing. Additional insights might be gleaned by a partnership between computer scientists, labor economists, and education researchers to assess the kinds of technology capabilities that are likely to emerge and diffuse in coming years, as well as opportunities for providing retraining and continuing education to workers.
Research in this area should aim to assess (1) educational and training needs based upon an understanding of evolving skills demands driven by technological change; (2) ways in which technology can be best used to prepare, train, and retrain the future workforce; and (3) the nature of technologies that can automate work (substituting for labor and existing human capital), augment it (complementing labor or requiring new skills), or transform it entirely (creating new goods, services, processes, and types of skill demand). Key research topics include educational needs, education delivery strategies, education access and incentives, technologies that can replace or complement worker skills, and broader educational policies.
Theme 7: The On-Demand Economy and Emerging Ways of Organizing Work
The emergence of the on-demand economy, in particular for ride-sharing services and crowdsourced work marketplaces, has generated great interest. However, there is little information about the extent of its impact on the economy and workforce. Research on the ability of authoritative economic and labor statistics to capture—and more comprehensive and persistent strategies for measuring—this impact are needed. In addition, research on the rights, protections, and autonomies of workers and how on-demand jobs fit into workers’ lives and careers is needed to
15 There is a strong likelihood that already disadvantaged groups will bear the brunt of the costs of automation. In addition, there is some evidence that a rise in disability rolls may, in part, reflect the role of automation in reducing the employment prospects for some groups. See D.H. Autor and M.G. Duggan, 2007, Distinguishing income from substitution effects in disability insurance, American Economic Review 97.2:119-124.
inform policies in this domain. In particular, this work could target the potential for technology-mediated on-demand jobs to provide or augment employment for unemployed or low-income workers.
Technology advances have helped shift the physical and geographical boundaries of work over time, with significant impacts on worker experience and job availability and access. Research in this area could elucidate the current and potential roles of technology in shifting where and how work is conducted, including changes in access to employment in geographically remote or isolated locations.
Theme 8: New Data Sources, Methods, and Infrastructures
All of the preceding themes would benefit from new data sources, methods, and infrastructures to enable the collection, aggregation, and distribution of a diverse range of data. The committee sees opportunities in the following areas:
- Updating and augmenting authoritative data sources to include survey questions and methods that directly probe technology-related aspects of employment and organizations.
- Developing new data sources and methods by creating new partnerships to provide researchers access to private-sector data, including new strategies for collecting and using born-digital data from multiple public and private sources and developing appropriate machine-learning and data-mining approaches to analyze this data. Research could also be conducted on providing alternate, more frequently updatable, and potentially even automated methods of obtaining information typically generated through cost-, labor-, and time-intensive survey methods.
- Combining micro- and macro-level data and methods, via establishment of research infrastructures and collaborations, to enable a comprehensive strategy for understanding the drivers of emerging trends and for testing hypotheses via both quantitative and qualitative approaches.
- Establishing new infrastructure and partnerships for aggregation, sharing, and collaboration to enable sharing among researchers of the large amounts of relevant digital data discussed above. Such efforts may be frustrated by existing and potentially outdated government regulations that constrain the ability of government to share certain data sets with researchers. While regulations to protect privacy of individuals are well justified, they may not reflect current approaches for protecting privacy while making data available for analysis. In any case, there is a general and persisting need for research on the technical means for protecting the privacy of individuals’ data, far beyond the specific research discussed in this report.
Progress in computing and information technologies has been rapid in recent years, and the pace of change is expected to continue or even accelerate in the foreseeable future. These technologies create opportunities for new products, services, organizational processes, and business models, and potential for automating existing tasks—both cognitive and physical—and even whole occupations. At the same time, new job opportunities are expected to emerge as increasingly capable combinations of humans and machines attack problems that previously have been intractable.
Advances in IT and automation will present opportunities to boost America’s overall income and wealth, improve health care, shorten the workweek, provide more job flexibility, enhance educational opportunities, develop new goods and services, and increase product safety and reliability. These same advances could also lead to growing inequality and decreased job stability, increasing demands on workers to change jobs, or major changes in business organization. More broadly, these technologies have important implications, both intended and unintended, in areas from education and social relationships to privacy, security, and even democracy.
The ultimate effects of these technologies are not predetermined. Rather, like all tools, computing and information technologies can be used in different ways. The outcomes for the workforce and society at large depend in part on the choices we make about how to use these technologies. New data and research advances will be critical for informing these choices.