Machine learning, Algorithmic fairness, Algorithmic accountability, ML4D

My research is focused on the risks and opportunities of algorithmic decision support in the context of sustainable societies. As part of my work, I characterize how societal biases encoded in historical data may be reproduced and amplified by ML models, and develop algorithms to mitigate these risks. Moreover, the sensitive task of creating decision support tools is complicated by several other factors, such as the fact that experts often care about constructs that are not well captured in the available labels. In my research, I aim to understand the limits and risks of using ML in these contexts, and to develop human-centered ML that can improve expert decision-making.