In his current role as Director of the Center for Data Science and Public Policy at the University of Chicago, Rayid Ghani teaches and works with governments and non-profits to help them use data to improve their decision-making and policies to create a better, equitable society.
Historically, Rayid’s research spanned from general machine learning and data science to privacy preserving data analysis, text analysis, semi-supervised learning, active learning, information retrieval, natural language processing, and knowledge management. This includes Rayid’s time as the Chief (Data) Scientist for the Obama 2012 Campaign, where he focused on Analytics, Technology, and Data for improving different functions of the campaign including fundraising, volunteer, and voter targeting and mobilisation.
Today, Rayid’s interest is in using computation, data and analytics for solving high impact social good problems in areas such as criminal justice, education, healthcare, energy, transportation, economic development, and public safety.
According to Rayid, “the use of data is critical in achieving a better, equitable society but what’s also critical is doing it legally, ethically, and the public having trust in the collection and use of their data. We cannot live in a world where data isn’t being used to make any decisions."
“Equally, we cannot live in a world where anyone can use anyone else’s data for anything. Reality needs to be somewhere in the middle, with legal and ethical guidelines, and with the public being a critical part of this conversation.”
A lot of government, civic, and non-profit organisations are realising the value of better data and have been focusing on improving data collection and data standardisation. Rayid’s role is to build on these efforts, and work with these organisations to use this data to help improve outcomes. This involves developing and using machine learning and social science methods that can be operationalised to solve policy and social challenges across health, criminal justice, education, public safety, criminal justice, social services, and economic development.
“The idea here is to combine interventions with prediction, because predictions by themselves aren’t doing anybody any good,” said Rayid.
In the Targeting Preventative Home Inspections to Reduce Childhood Lead Poisoning Project, Rayid and his colleagues combined 15 years of data from home inspections and tests of blood lead levels in children in Chicago to create a model that predicts the risk of lead poisoning. The model is now integrated with one hospital system's electronic health records, and it flags pregnant patients who live at addresses with potentially hazardous lead levels.
"Now the health department has a few months to figure out how to allocate resources to fix this problem before the kid is born," Rayid said.
“Most machine-learning classes tend to focus on abstract problems, while people with expertise in social issues are often unfamiliar with how to find data-driven solutions.
It’s important for data models to include humans because if people don’t trust the system, they won’t use it. Also, even good systems aren’t correct in a significant share of cases — human knowledge and judgment can correct for that. Plus, people know which interventions can best help those identified by computer models," Rayid reveals.
Rayid has also authored two books ‘Big Data and Social Science: Theory and Practical Approaches’ from CRC Press and ‘Data Mining for Business Applications’ from IOS Press.
Rayid will be presenting the keynote ‘Making a difference: Data Science for Social Good’ at the IAPA National Conference, Advancing Analytics on 18 October 2018.