Suresh Venkatasubramanian (University of Utah), Nadya Bliss (Arizona State University), Helen Nissenbaum (Cornell University), and Melanie Moses (University of New Mexico) Overview Long gone are the days when computing was the domain of technical experts. We live in a world where computing technology--especially artificial intelligence--permeates every aspect of our daily lives, playing a significant role in augmenting and even replacing human decision-making in a broad range of situations. AIenabled technologies can adjust to your child's level of understanding by processing a pattern of mistakes; AI systems can leverage combinations of sensor inputs to choose and carry out braking actions in your car; web browsers with AI capabilities can reason from past observations of your searches to recommend a new cuisine in a new location. Innovations in AI have focused primarily on the questions of "what" and "how"--algorithms for finding patterns in web searches, for instance--without adequate attention to the possible harms (such as privacy, bias, or manipulation) and without adequate consideration of the societal context in which these systems operate. As a result of this tight technical focus, and the rapid, worldwide explosion in its use, AI has come with a storm of unanticipated socio-technical problems, ranging from algorithms that act in racially or gender-biased ways, get caught in feedback loops that perpetuate inequalities, or enable unprecedented behavioral monitoring surveillance that challenges the fundamental values of free, democratic societies.
"As we honor the more mathematical, abstract, and scientific' parts of our subject more, and the practical parts less, we misdirect the young and brilliant minds away from a body of challenging and important problems that are our peculiar domain, depriving these problems of the powerful attacks they deserve." I have the privilege of working at the Defense Advanced Research Projects Agency (DARPA) and currently serve as the Acting Deputy Director of the Defense Sciences Office (DSO). Our goal at DARPA is to create and prevent technological surprise through investments in science and engineering, and our history and contributions are well documented. The DSO is sometimes called "DARPA's DARPA," because we strive to be at the forefront of all of science--on the constant lookout for opportunities to enhance our national security and collective well-being, and our projects are very diverse. One project uses cold atoms to measure time with 10 18th precision; another is creating amazing composite materials that can change the way in which we manufacture.
How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresses the issue of co-constructing a model when several disciplines are involved, and is applied to modelling human behaviour immediately after an earthquake. The main contribution of the work is to propose a tool dedicated to multidisciplinary dialogue. It also proposes a reflexive analysis of the enriching intellectual process carried out by the different disciplines involved. Finally, from working with an anthropologist, a complementary view of the multidisciplinary process is given.
The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?