  If machine learning models were to achieve superhuman abilities at various
reasoning or decision-making tasks, how would we go about evaluating such
models, given that humans would necessarily be poor proxies for ground truth?
In this paper, we propose a framework for evaluating superhuman models via
consistency checks. Our premise is that while the correctness of superhuman
decisions may be impossible to evaluate, we can still surface mistakes if the
model's decisions fail to satisfy certain logical, human-interpretable rules.
We instantiate our framework on three tasks where correctness of decisions is
hard to evaluate due to either superhuman model abilities, or to otherwise
missing ground truth: evaluating chess positions, forecasting future events,
and making legal judgments. We show that regardless of a model's (possibly
superhuman) performance on these tasks, we can discover logical inconsistencies
in decision making. For example: a chess engine assigning opposing valuations
to semantically identical boards; GPT-4 forecasting that sports records will
evolve non-monotonically over time; or an AI judge assigning bail to a
defendant only after we add a felony to their criminal record.
