CAREER: Robust, Interpretable, and Fair Allocation of Scarce Resources in Socially Sensitive Settings

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by improving critical public sector systems that allocate scarce resources to satisfy basic needs. These systems operate in complex, uncertain, time-dependent environments, and as public sector services, they must be transparent, satisfy potentially conflicting stakeholder objectives, and be constructed to perform as intended in a variety of environments when deployed.

To address these problems, this project constructs a computationally efficient framework to design policies that are robust to uncertainty, interpretable, and fair. The systems will learn and correctly balance stakeholder value judgements and account for underlying biases, incentives, and disparities. The central use case that will guide the research will focus on allocating scarce housing resources to those experiencing homelessness.

The project will be facilitated by a collaboration with the Los Angeles Homeless Services Authority and with homelessness experts. The plan to integrate research and education includes the design of a new course on “Analytics for Social Impact” and of an online experimental platform to educate students and the general public about resource allocation in socially sensitive settings.

Outreach activities will be focused on the promotion of diversity, equity, and inclusion in STEM fields and include a long-term partnership with the STEM Academy of Hollywood and a new collaboration with the Code.org non-profit.

This research will advance data-driven robust optimization models that cope well with information incompleteness and non-stationarity, and derive tractable models that offer probabilistic performance guarantees. This project will provide a methodology for learning and aggregating incomplete and conflicting stakeholder preferences and offer new fair machine learning (ML) algorithms.

The research will develop a novel robust queuing theory framework that leverages learned preferences, outcome predictions, and observational data to design policies guaranteed to work as planned when deployed in the open world. Finally, the project will provide tools that help committees evaluate policies, anticipate their consequences, and understand the trade-offs between fairness, efficiency, and interpretability.

The framework contributes to the robust optimization literature in several regards. It provides a modeling and solution scheme for multi-stage robust optimization problems with decision-dependent information discovery, exponentially many contingencies, and non-linear objective. It is the first study on models and methods for robust and fair ML.

Finally, it provides a method for performing counterfactual policy evaluation and optimization from noisy observational data in the robust queuing theory framework. The research contributes to AI and marketing with new optimization-based techniques for preference elicitation and aggregation, to ML with general purpose robust and fair tools, and to queuing theory with causal inference-based approaches for system evaluation and design.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.