Research Areas

Our research initiatives are motivated by working on hands-on data science projects with governments, non-profits, and other policy organizations. As we tackle policy problems, we identify open areas where existing methods from computer science, machine learning, artificial intelligence  or social sciences are lacking and formulate our research initiatives to fill those gaps. We then push the results of our research back into our data science tools so they can be used across our projects and by our project partners. We are currently working on:

  • Auditing  and Correcting for Bias and Equity Issues in Data Science Systems
  • Increasing the interpretability and transparency of machine learning models used in policy decisions
  • Designing experimental validation methodologies for machine learning systems
  • Developing methods for monitoring and updating deployed data science systems