>>>>>>The deadline for papers and abstracts submission has been EXTENDED TO January 27th 2020.<<<<<< The need for social innovation is clear: From climate change to income inequality to geopolitical upheaval and terrorism, the difficulties confronting us are unprecedented not only in their variety but also in their complexity. At the same time, today’s public policy practices and tools are not sufficient. Increasingly, it is clear, we need not only new solutions but new methods to arriving at solutions.
Data and data science will become more central to meeting these challenges and to social innovation, philanthropy, international development and humanitarian aid. From the analysis of satellite imagery to mapping poverty to using Facebook data to track the global digital gender gap, “Data Science for Social Good” provides great promise. Data from corporate actors (e.g. mobile phones data, remote sensing, satellite imagery) as well as data from digital traces generated by the pervasiveness of the Web in combination with state-of-the-art knowledge generated by data science can be synergically exploited to solve issues around many social problems and support global agencies and policymakers in implementing better and more impactful policies and interventions.
Yet, for all of data’s potential to address public challenges, the truth remains that most of the data assets and data science capabilities available today are not yet sufficiently applied to solving public problems. Because of a lack of awareness of the potential, funding and data access restrictions and often poorly distributed data science capacity, its vast potential often goes untapped.
This workshop will review the potential and emerging field of data science for good – as well as how to develop new partnerships for the data age (Data Collaboratives) to unlock both data and data science capabilities.
Key questions we will seek to address through this workshop include:
What are the value propositions and practices of data science for social good?
What are different ways to engage data scientists (e.g. through data collaboratives) - examples and lessons learned?
What are the risks of using data science for social good?
What data science methodologies are already available or need to be accelerated to solve public problems?
How to overcome the current transactions costs and funding constraints associated with leveraging data and data science for social good?
What are the ethical considerations and risks of using data science for social good?