Understanding Accuracy-Efficiency Trade-Offs as a Means for Holding Distributed ML Systems Accountable

Trade-offs between accuracy and efficiency are found in multiple non-computing domains, such as law and public health, which have developed rules and heuristics to guide how to balance the two in conditions of uncertainty. While accuracy-efficiency trade-offs are also commonly acknowledged in some areas of computer science, their policy implications remain poorly examined. Drawing on risk assessment practices in the US, we argue that, since examining accuracy-efficiency trade-offs has been useful for guiding governance in other domains, explicitly framing such trade-offs in computing is similarly useful for the governance of computer systems. Our discussion focuses on real-time distributed ML systems; understanding the policy implications in this area is particularly urgent because such systems, which include autonomous vehicles, tend to be high-stakes and safety-critical. We describe how the trade-off takes shape for these systems, highlight gaps between existing US risk assessment standards and what these systems require in order to be properly assessed, and make specific calls to action to facilitate accountability when hypothetical risks become realized as accidents in the real world. We close by discussing how such accountability mechanisms encourage more just, transparent governance aligned with public values.