Machine learning based decision making systems applied in safety critical
areas require reliable high certainty predictions. For this purpose, the system
can be extended by an reject option which allows the system to reject inputs
where only a prediction with an unacceptably low certainty would be possible.
While being able to reject uncertain samples is important, it is also of
importance to be able to explain why a particular sample was rejected. With the
ongoing rise of eXplainable AI (XAI), a lot of explanation methodologies for
machine learning based systems have been developed -- explaining reject
options, however, is still a novel field where only very little prior work
exists.
In this work, we propose to explain rejects by semifactual explanations, an
instance of example-based explanation methods, which them self have not been
widely considered in the XAI community yet. We propose a conceptual modeling of
semifactual explanations for arbitrary reject options and empirically evaluate
a specific implementation on a conformal prediction based reject option.

本文提出利用半事实解释拒绝选项的概念模型，以实现机器学习中的可解释性，并通过实验对拒绝选项进行了评估。

拒绝的多元半事实性解释

"Even if ..." -- Diverse Semifactual Explanations of Reject

Machine learning models always make a prediction, even when it is likely to
be inaccurate. This behavior should be avoided in many decision support
applications, where mistakes can have severe consequences. Albeit already
studied in 1970, machine learning with a reject option recently gained
interest. This machine learning subfield enables machine learning models to
abstain from making a prediction when likely to make a mistake.
This survey aims to provide an overview on machine learning with a reject
option. We introduce the conditions leading to two types of rejection,
ambiguity and novelty rejection. Moreover, we define the existing architectures
for models with a reject option, describe the standard learning strategies to
train such models and relate traditional machine learning techniques to
rejection. Additionally, we review strategies to evaluate a model's predictive
and rejective quality. Finally, we provide examples of relevant application
domains and show how machine learning with rejection relates to other machine
learning research areas.

此调查研究了具有拒绝选项的机器学习，介绍了拒绝的两种类型，现有模型的结构和训练策略，以及如何评估模型的预测能力和拒绝质量，并提供了相关应用领域的示例。

带有拒绝选项的机器学习：一份调查

Machine Learning with a Reject Option: A survey

We consider the problem of selective prediction (also known as reject option)
in deep neural networks, and introduce SelectiveNet, a deep neural architecture
with an integrated reject option. Existing rejection mechanisms are based
mostly on a threshold over the prediction confidence of a pre-trained network.
In contrast, SelectiveNet is trained to optimize both classification (or
regression) and rejection simultaneously, end-to-end. The result is a deep
neural network that is optimized over the covered domain. In our experiments,
we show a consistently improved risk-coverage trade-off over several well-known
classification and regression datasets, thus reaching new state-of-the-art
results for deep selective classification.

本论文介绍了 SelectiveNet，这是一种有集成拒绝选项的深度神经架构，通过同时优化分类（或回归）和拒绝，得到了一个在覆盖域上优化的深度神经网络，实验表明，SelectiveNet 在多个分类和回归数据集上均获得了改进的风险覆盖权衡效果，从而达到了深度选择分类的新的最佳结果。

SelectiveNet：具有集成拒绝选项的深度神经网络

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

In many real applications of statistical learning, a decision made from
misclassification can be too costly to afford; in this case, a reject option,
which defers the decision until further investigation is conducted, is often
preferred. In recent years, there has been much development for binary
classification with a reject option. Yet, little progress has been made for the
multicategory case. In this article, we propose margin-based multicategory
classification methods with a reject option. In addition, and more importantly,
we introduce a new and unique refine option for the multicategory problem,
where the class of an observation is predicted to be from a set of class
labels, whose cardinality is not necessarily one. The main advantage of both
options lies in their capacity of identifying error-prone observations.
Moreover, the refine option can provide more constructive information for
classification by effectively ruling out implausible classes. Efficient
implementations have been developed for the proposed methods. On the
theoretical side, we offer a novel statistical learning theory and show a fast
convergence rate of the excess $\ell$-risk of our methods with emphasis on
diverging dimensionality and number of classes. The results can be further
improved under a low noise assumption. A set of comprehensive simulation and
real data studies has shown the usefulness of the new learning tools compared
to regular multicategory classifiers. Detailed proofs of theorems and extended
numerical results are included in the supplemental materials available online.

本文提出了一种基于边际的带拒绝选项的多类别分类方法，引入了一种新颖的改进选项，可以识别出易错观测值，并且在低噪音假设下可优化统计学习理论与方法。