Devansh Jalota

Algorithmic decision-making systems are increasingly influencing the allocation of scarce resources and have demonstrated tremendous potential in improving the effectiveness of such market mechanisms. However, a vast majority of such systems (e.g., congestion pricing for road traffic mitigation) remain unimplemented as some users are made disproportionately worse off in the pursuit of system efficiency. Even worse, the efficacy of such mechanisms relies on access to complete information on users’ attributes and preferences, which may not be available in practice. I aim to address the inadequacy of existing resource allocation approaches by developing market mechanisms that cater to the needs of all users while harnessing the ever-increasing availability of data to better inform allocation decisions. In doing so, my work will help address the equitability concerns of existing mechanisms and help lay the groundwork for the practical deployment of algorithmic decision-making systems in resource allocation applications.