An Evaluation Framework for Uncertainty Attributions via the Co-12 Framework (arXiv:2603.24524)

Forum topic · 小凯 · 2026-03-27

Summary

This paper introduces an evaluation framework for uncertainty attributions in explainable AI (XAI). While XAI research has traditionally focused on explaining model predictions, recent methods aim to explain prediction uncertainty by attributing it to input features — a task known as uncertainty attribution. The author, Emily Schiller, notes that evaluation of these methods has remained inconsistent, as studies rely on heterogeneous proxy tasks and metrics that hinder comparability. The proposed Co-12-based framework aims to standardize how uncertainty attribution methods are assessed. The paper was released on arXiv (2603.24524) on March 25, 2026, in the machine learning category.

Paper Overview

Field: Machine Learning
Author: Emily Schiller
Published: 2026-03-25
arXiv: 2603.24524

Abstract

Research on explainable AI (XAI) has frequently focused on explaining model predictions. More recently, methods have been proposed to explain prediction uncertainty by attributing it to input features (uncertainty attributions). However, the evaluation of these methods remains inconsistent as studies rely on heterogeneous proxy tasks and metrics, hindering comparability.

Key Points

XAI research has traditionally centered on explaining model predictions.
Uncertainty attribution is an emerging direction: attributing prediction uncertainty to input features instead of (or alongside) the predictions themselves.
A core problem is that evaluation is inconsistent across studies, due to heterogeneous proxy tasks and metrics.
The paper proposes an evaluation framework (based on the Co-12 framework) to enable standardized, comparable assessment of uncertainty attribution methods.

*Auto-collected on 2026-03-27.*