This project will support a two-day workshop entitled "The Social, Economic and Workforce Implications of Big Data Analysis and Decision-Making" to be held in January 2014. Big Data, the analysis and application of very large datasets, is an important new area of computer science research that has given rise to a number of new journals, conferences and funding opportunities as well as great interest in practice. Public and private sectors organizations of all kinds, including the National Science Foundation, are making huge investments in Big Data in the well-justified belief that innovations in data analytics can bring enormous benefits in such areas as public health and safety, economic competitiveness and consumer welfare. At the same time, history makes clear that innovations do not always realize their intended benefits and that they sometimes have negative (in addition to positive) unintended consequences.
The goal of the workshop is to develop an agenda for research on the Social, Economic and Workforce consequences of Big Data to mobilize research momentum in this important area.
The workshop will bring together policy makers and subject matter experts representing a range of relevant disciplines and perspectives, including computer science, economics, social sciences, and philosophy. The workshop will be organized as plenary and breakout sessions, organized around four major themes: 1) consequences for citizens and social life (e.g., democratic participation and interpersonal interaction), 2) consequences for employment and economic opportunity (e.g., job availability and quality and access to credit and investment opportunities), 3) consequences for science and innovation (e.g., scientific practices and rewards and the structure of R&D), and 4) consequences for critical infrastructure (e.g., public health and safety and financial system stability).
Intellectual Merit: The workshop will advance computer science and engineering by developing an agenda for systemic research to document emerging consequences and to anticipate plausible future consequences of Big Data. The science of the social, economic, and workforce consequences of revolutionary technological innovations like Big Data is challenging for at least two reasons. First, it is inherently interdisciplinary. Second, to be useful, such a science must be anticipatory as well as post-hoc, but criteria and approaches for evaluating forward-looking research are still nacent.
Broader Impacts: Big data is growing in importance for many areas critical to society, including health, finance and science. However, current research is focused primarily on the underlying technology, leaving the societal, economic and workforce impacts largely unexamined. The outputs of this workshop will help researchers identify the key issues in understanding these impacts and articulate possible approaches for future research.
Executive Summary An NSF award was made for hosting a multidisciplinary workshop on the social, ethical, economic, and workforce implications of Big Data. Invited academics from computer science, economics, ethics, information systems, organizational behavior, law, and sociology met with members of the business community and representatives of research funding agencies and foundations in Washington, DC, in January 2014, to discuss research topics and priorities. The focus of the workshop was on research about the implications and consequences of Big Data, rather than on research that uses or builds large datasets, analysis techniques, or technology infrastructures. Workshop participants defined Big Data to include not just data, but also the models and algorithms used to analyze data, and the individual and organizational decisions made on the basis of data. Because of its growing pervasiveness, Big Data is a potentially transformative innovation. That is, it can lead to disruptive changes by displacing older socioeconomic activities and creating entirely new opportunities. Historically, disruptive transformations have brought many benefits but also some intractable problems, like pollution. The aim of systematic Big Data implications research is to maximize Big Data’s socioeconomic benefits while minimizing negative consequences. Participants in the workshop charted a middle course between the two extremes of Big Data enthusiasm and Big Data skepticism. The potential benefits and risks of Big Data were clearly acknowledged. At the same time, there was broad consensus that systematic research on the implications and consequences of Big Data can be highly beneficial by 1) allaying unfounded fears through careful analysis of Big Data’s realized or plausible costs, benefits, and risks and their distributions across stakeholder groups, 2) identifying or evaluating alternative technical and nontechnical strategies for reducing Big Data’s actual or plausible harms and for reducing barriers to the achievement of Big Data’s benefits and, 3) generating or testing new approaches for increasing Big Data’s economic and public value through better tools and techniques, improved services and business models, enhanced work practices, and augmented educational programs. Discussions covered three overlapping contexts of Big Data use: 1) science and technology policy and scientific research, including open government data and university-based research, 2) everyday life, including civic engagement with Big Data through citizen science and data "hackathons" or use of social media and fitness applications, and 3) organizations and work, including commercial applications of Big Data in engineering, marketing, and business operations, and the application and use of Big Data by data scientists and knowledge workers. Use of Big Data in the national security context was explicitly excluded from discussion at the workshop. In the area of science and science-technology policy, workshop participants identified three high-priority research areas: 1) the lessons learned from new data-oriented partnerships among government, universities, businesses, and civil society organizations, including data partnerships created under the auspices of President Obama’s Big Data initiative 2) the practices and consequences of the new data economy, including how data are monetized and sold and the economic and public value created by data-oriented initiatives and 3) Big Data’s implications for universities, academic disciplines, and research publishing. Three key research priorities related to individuals and everyday life were discussed at the workshop: 1) new ways of thinking about privacy and privacy protection, given that the threats to personal information privacy have shifted from official government and business records to new data sources, including social media, product sensors, and body cameras 2) the careers, activities, and contributions of "amateur" citizen data scientists, because formal education is not the only route into technical occupations, and 3) Big Data’s personal benefits, e.g., better decision-making, and its personal burdens, such as new forms of "social sorting" and discrimination. The third domain in which workshop participants discussed research priorities was that of organizations and work. Participants noted that far more attention has been paid to the implications of Big Data for the science and civic domains than for the domain of corporations and their employees. Yet this domain is highly significant for innovation, employment, and national productivity and competitiveness. Three research priorities in this domain were identified: 1) the skill requirements and professional conduct of corporate data scientists 2) the ways in which corporations manage data and algorithms and make decisions about Big Data initiatives and decision automation, and 3) the changes in jobs, knowledge, and expertise that Big Data may bring.
