Algorithmic Robustness
David Jensen Brian LaMacchia Ufuk Topcu Pamela Wisniewski
Algorithmic robustness refers to the sustained performance of a computational
system in the face of change in the nature of the environment in which that
system operates or in the task that the system is meant to perform. Below, we
motivate the importance of algorithmic robustness, present a conceptual
framework, and highlight the relevant areas of research for which algorithmic
robustness is relevant. Why robustness? Robustness is an important enabler of
other goals that are frequently cited in the context of public policy decisions
about computational systems, including trustworthiness, accountability,
fairness, and safety. Despite this dependence, it tends to be under-recognized
compared to these other concepts. This is unfortunate, because robustness is
often more immediately achievable than these other ultimate goals, which can be
more subjective and exacting. Thus, we highlight robustness as an important
goal for researchers, engineers, regulators, and policymakers when considering
the design, implementation, and deployment of computational systems. We urge
researchers and practitioners to elevate the attention paid to robustness when
designing and evaluating computational systems. For many key systems, the
immediate question after any demonstration of high performance should be: "How
robust is that performance to realistic changes in the task or environment?"
Greater robustness will set the stage for systems that are more trustworthy,
accountable, fair, and safe. Toward that end, this document provides a brief
roadmap to some of the concepts and existing research around the idea of
algorithmic robustness.