Our research initiatives are motivated by working on applied 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 tools so they can be used across our projects, by our project partners, and by the larger community. We are currently working on:
- Dealing with Bias and Equity Issues in Designing, Building, and Deploying AI/ML/Data Science Systems
- Increasing the interpretability of machine learning models used in policy decisions
- Designing experimental validation methodologies for creating robust and trustworthy machine learning systems
- Developing methods for monitoring and updating deployed data science systems
- Combining machine-learning techniques with social science and behavioral psychology for large-scale data-driven behavior change problems.