Tools for Enabling Large-Scale Public Engagement in Research

Non-scientists have long been contributing to research: by gathering observations on plant and animal behavior, by gazing at the sky through private amateur telescopes, or by participating in psychology experiments.  The Internet has created entirely new opportunities for enabling public participation in research, both in terms of the scale of public participation and the kinds of activities that the non-professional scientists can perform in support of scientific inquiry.

We are looking for Fellows, likely with backgrounds in HCI, AI, or Educational Technology, who wish to lead new projects in this broad area.  Examples of possible areas of inquiry include:

  • Mechanisms for creating and sustaining engagement in scientifically-valuable activities
  • Algorithms for optimally leveraging contributions from participants with vastly different levels of skill and expertise
  • Integrating participation in research with educational opportunities
  • Methodologies for ensuring reliability and validity of data collected from large, self-selected, heterogeneous populations
  • Engaging with domain scientists in other disciplines to solve valuable research problems that could not be feasibly pursued without massive public participation

Two specific ongoing projects illustrate our efforts so far:

Our Lab in the Wild project explores mechanisms for engaging a broad and diverse public as participants in human subjects experiments.  In a little over a year, Lab in the Wild has attracted nearly a million active participants.  This project has allowed us to explore mechanisms for creating and sustaining engagement, mechanisms for ensuring and verifying reliability of data in unsupervised human subjects experiments, new methodological challenges for analyzing results from large heterogeneous samples, and approaches for communicating partial results to the public.

Our brand new Curio project aims to enable professional and amateur scientists to collaborate directly on collecting, processing, and analyzing scientific data.   In-depth interviews with nearly twenty scientists representing disciplines ranging from humanities to social sciences, to life sciences and engineering have given us insights into the opportunities for large-scale public engagement in data-intensive scientific projects.  These interviews have also helped us uncover cultural, ethical, legal, and technical barriers that need to be overcome.  One technical challenge that we are pursuing in the context of the Curio platform is the development of machine learning--based techniques for automatically translating professional scientists’ goals into workflows composed of tasks that amateur scientists can perform reliably.