As deep neural networks are more commonly deployed in high-stakes domains,
their lack of interpretability makes uncertainty quantification challenging. We
investigate the effects of presenting conformal prediction
sets$\unicode{x2013}$a method for generating valid confidence sets in
distribution-free uncertainty quantification$\unicode{x2013}$to express
uncertainty in AI-advised decision-making. Through a large pre-registered
experiment, we compare the utility of conformal prediction sets to displays of
Top-1 and Top-k predictions for AI-advised image labeling. We find that the
utility of prediction sets for accuracy varies with the difficulty of the task:
while they result in accuracy on par with or less than Top-1 and Top-k displays
for easy images, prediction sets excel at assisting humans in labeling
out-of-distribution (OOD) images especially when the set size is small. Our
results empirically pinpoint the practical challenges of conformal prediction
sets and provide implications on how to incorporate them for real-world
decision-making.

在高风险领域中部署深度神经网络时，由于缺乏可解释性，不确定性量化变得具有挑战性。本文通过大规模预注册实验，比较了使用符合性预测集合与 Top-1 和 Top-k 预测展示相比在 AI 辅助图像标注中表现的优势，并发现对易任务来说，预测集合与 Top-1 和 Top-k 展示的准确性相当或略少，但在标记超出分布范围的图像时，特别是当集合大小较小时，预测集合能够卓越地帮助人类进行标注。研究结果从实证角度指出符合性预测集合的实际挑战，并提供了将其纳入实际决策制定的启示。

评估基于变换预测集的 AI 辅助图像标注的实用性

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image  Labeling

Motivated by the need for the development of safe and reliable methods for
uncertainty quantification in machine learning, I propose and develop ideas for
a model-free statistical framework for imprecise probabilistic prediction
inference. This framework facilitates uncertainty quantification in the form of
prediction sets that offer finite sample control of type 1 errors, a property
shared with conformal prediction sets, but this new approach also offers more
versatile tools for imprecise probabilistic reasoning. Furthermore, I propose
and consider the theoretical and empirical properties of a precise
probabilistic approximation to the model-free imprecise framework.
Approximating a belief/plausibility measure pair by an [optimal in some sense]
probability measure in the credal set is a critical resolution needed for the
broader adoption of imprecise probabilistic approaches to inference in
statistical and machine learning communities. It is largely undetermined in the
statistical and machine learning literatures, more generally, how to properly
quantify uncertainty in that there is no generally accepted standard of
accountability of stated uncertainties. The research I present in this
manuscript is aimed at motivating a framework for statistical inference with
reliability and accountability as the guiding principles.

基于可靠性和可问责性的指导原则，本文提出了一种模型无关的统计框架，用于不确定性量化和统计推断，通过预测集提供可控制第一类错误的有限样本控制方法，并提供了灵活的工具用于不确定性概率推理。

非模型广义基准推断

Model-free generalized fiducial inference

Most classifiers operate by selecting the maximum of an estimate of the
conditional distribution $p(y|x)$ where $x$ stands for the features of the
instance to be classified and $y$ denotes its label. This often results in a
{\em hubristic bias}: overconfidence in the assignment of a definite label.
Usually, the observations are concentrated on a small volume but the classifier
provides definite predictions for the entire space. We propose constructing
conformal prediction sets which contain a set of labels rather than a single
label. These conformal prediction sets contain the true label with probability
$1-\alpha$. Our construction is based on $p(x|y)$ rather than $p(y|x)$ which
results in a classifier that is very cautious: it outputs the null set ---
meaning "I don't know" --- when the object does not resemble the training
examples. An important property of our approach is that adversarial attacks are
likely to be predicted as the null set or would also include the true label. We
demonstrate the performance on the ImageNet ILSVRC dataset and the CelebA and
IMDB-Wiki facial datasets using high dimensional features obtained from state
of the art convolutional neural networks.

本文提出了基于条件概率密度 p (x|y) 而非 p (y|x) 的符合性预测集合（conformal prediction sets）来消除分类模型中的傲慢偏差（hubristic bias），该模型在不确定时会输出空集，能很好地应对对抗攻击（adversarial attacks）。在 ImageNet ILSVRC 数据集和 CelebA、IMDB-Wiki 人脸数据集上以高维卷积神经网络为特征进行的实验结果表明它的性能优秀。