AGENTIC-SYSTEMSMay 22, 20261 min read9 views

$ECUAS_n$: A family of metrics for principled evaluation of uncertainty-augmented systems

Researchers propose $ECUAS_n$, a novel family of metrics designed to evaluate uncertainty-augmented AI systems, optimizing decision-making in high-stakes scenarios by balancing the costs of incorrect predictions and imperfect uncertainties.

Computer Science > Artificial Intelligence

Title:$ECUAS_n$: A family of metrics for principled evaluation of uncertainty-augmented systems

View PDF HTML (experimental)Abstract:In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems -- to accept or reject predictions based on application-specific cost trade-offs. Such uncertainty-augmented (UA) systems -- i.e., systems that output both predictions and uncertainty scores -- are currently being assessed in the literature in a variety of ways, using separate metrics to evaluate the predictions and the uncertainty scores, setting a cost function with a fixed rejection cost or integrating over a coverage-risk curve. We argue that these evaluation approaches are inadequate for assessing overall performance of the UA system for decision making under uncertainty and propose a novel family of metrics, $ECUAS_n$, formulated as proper scoring rules for the task of interest. The parameter $n$ controls the trade-off between the cost of incorrect predictions and imperfect uncertainties depending on the needs of the use-case. We demonstrate the advantages of the $ECUAS_n$ metrics both theoretically and empirically, through experiments on diverse classification and generation datasets, including a manually annotated subset of TriviaQA.

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Source: arXiv cs.AI Recent

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