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While inquiry in operations research (OR) modeling of urban planning processes is long-standing, on the whole, the OR discipline has not influenced urban planning practice, teaching and scholarship at a level of other domains such as public policy and information technology. Urban planning presents contemporary challenges that are complex, multi-stakeholder, data-intensive, and ill structured. Could an OR approach which focuses on the complex, emergent nature of cities, the institutional environment in which urban planning strategies are designed and implemented and which puts citizen engagement and a critical approach at the center enable urban planning to better meet these challenges? Based on a review of research and practice in OR and urban planning, we argue that a prospective and prescriptive approach to planning that is inductive in nature and embraces “methodological pluralism” and mixed methods can enable researchers and practitioners develop effective interventions that are equitable and which reflect the concerns of community members and community serving organizations. We discuss recent work in transportation, housing, and community development that illustrates the benefits of embracing an enhanced OR modeling approach both in the framing of the model and in its implementation, while bringing to the fore three cautionary themes. First, a mechanistic application of decision modeling principles rooted in stylized representations of institutions and systems using mathematics and computational methods may not adequately capture the central role that human actors play in developing neighborhoods and communities. Second, as innovations such as the mass adoption of automobiles decades ago led to auto-centric city design show, technological innovations can have unanticipated negative social impacts. Third, the current COVID pandemic shows that approaches based on science and technology alone are inadequate to improving community lives. Therefore, we emphasize the important role of critical approaches, community engagement and diversity, equity, and inclusion in planning approaches that incorporate decision modeling. 

The use of automated data-driven tools for decision-making has gained popularity in recent years. At the same time, the reported cases of algorithmic bias and discrimination increase as well, which in turn lead to an extensive study of algorithmic fairness. Numerous notions of fairness have been proposed, designed to capture different scenarios. These measures typically refer to a "protected group" in the data, defined using values of some sensitive attributes. Confirming whether a fairness definition holds for a given group is a simple task, but detecting groups that are treated unfairly by the algorithm may be computationally prohibitive as the number of possible groups is combinatorial. We present a method for detecting such groups efficiently for various fairness definitions. Our solution is implemented in a system called DENOUNCER, an interactive system that allows users to explore different fairness measures of a (trained) classifier for a given test data. We propose to demonstrate the usefulness of DENOUNCER using real-life data and illustrate the effectiveness of our method. 

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: They can inform local, state, and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.