RIIPL and Professor Ellen P. Goodman welcome leading scholars in technology law, journalism, information studies, sociology, urban studies, and architecture September 14-15, 2017 to a workshop on:


The goal of the project is to improve understanding of how big data sets lead to algorithmic predictions, which can shape the provision of government and private services and the distribution of opportunities. The project will focus on access and transparency issues related to algorithmic learning.  Such algorithmic learning impacts government decisions around energy policy, educational policy, policing, transportation policy, smart cities deployment, employment, and many other areas. We will work collaboratively on research regarding such issues as: *        Open records and FOIA as tools for algorithmic and data access *        The production of information graphics to facilitate public understanding of resource allocation, political processes, and threat responses *        Private capture of public information and public access to such data *        Sustainability and environmental data access *        Smart cities, private-public partnerships, and the risk of the “Uberization” of public data (meaning public entities trading control over data in return for private entity provision of public services) *        Criminal justice data and the rights of criminal defendants and communities to access about criminal justice and policing decisions *        The use of propaganda to obfuscate and impede access to information, and techniques to push back