What should an AI assessor optimise for?
Keywords: Assessor Models, Predictable AI, Regression problems, Error metrics
TL;DR: We explore whether training AI assessors on a different loss function than the target loss can outperform direct optimization, finding that certain monotonic transformations, like logistic loss, offer surprising benefits.
Abstract: An AI assessor is an external, ideally independent system that predicts an indicator, e.g., a loss value, of another AI system. Assessors can leverage information from the test results of many other AI systems and have the flexibility of being trained on any loss function: from squared error to toxicity metrics. Here we address the question: is it always optimal to train the assessor for the target loss? Or could it be better to train for a different loss and then map predictions back to the target loss? Using ten regression problems with tabular data, we experimentally explore this question for regression losses with monotonic and nonmonotonic mappings and find that, contrary to intuition, optimising for more informative losses is not generally better. Surprisingly though, some monotonic transformations, such as the logistic loss used to minimise the absolute or squared error, are promising.
Primary Area: datasets and benchmarks
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Submission Number: 4285
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