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Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or saturates soon. How can we then evaluate machines that may become more capable than the current machines and the people who write the tests? One answer to the question hinges...
New approaches to evaluating progress of AI should be built around diversity, partnership, and systemicity.
This work experimentally explores the question: is it always optimal to train the assessor for the target metric, or could it be better to train for a different metric and then map predictions back to the target metric?
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