$ECUAS_n$: A family of metrics for principled evaluation of uncertainty-augmented systems
The paper introduces $ECUAS_n$, a new family of metrics designed for evaluating uncertainty-augmented systems in automated decision-making. These metrics aim to provide a more comprehensive assessment of both predictions and uncertainty scores, addressing limitations in current evaluation methods. The authors demonstrate the effectiveness of $ECUAS_n$ through theoretical and empirical analyses across various datasets.
- ▪$ECUAS_n$ metrics are formulated as proper scoring rules tailored for specific tasks.
- ▪The parameter $n$ in $ECUAS_n$ allows for adjusting the trade-off between the costs of incorrect predictions and imperfect uncertainties.
- ▪The paper presents experimental results on diverse datasets, including a subset of TriviaQA.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20490 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Artificial Intelligence arXiv:2605.20490 (cs) [Submitted on 19 May 2026 (v1), last revised 21 May 2026 (this version, v2)] Title:$ECUAS_n$: A family of metrics for principled evaluation of uncertainty-augmented systems Authors:Lautaro Estienne, Erik Ernst, Matías Vera, Pablo Piantanida, Luciana Ferrer View a PDF of the paper titled $ECUAS_n$: A family of metrics for principled evaluation of uncertainty-augmented systems, by Lautaro Estienne and 4 other authors 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.
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