In recent years, there has been significant work on increasing both
interpretability and debuggability of a Deep Neural Network (DNN) by extracting
a rule-based model that approximates its decision boundary. Nevertheless,
current DNN rule extraction methods that consider a DNN's latent space when
extracting rules, known as decompositional algorithms, are either restricted to
single-layer DNNs or intractable as the size of the DNN or data grows. In this
paper, we address these limitations by introducing ECLAIRE, a novel
polynomial-time rule extraction algorithm capable of scaling to both large DNN
architectures and large training datasets. We evaluate ECLAIRE on a wide
variety of tasks, ranging from breast cancer prognosis to particle detection,
and show that it consistently extracts more accurate and comprehensible rule
sets than the current state-of-the-art methods while using orders of magnitude
less computational resources. We make all of our methods available, including a
rule set visualisation interface, through the open-source REMIX library
(this https URL).

本篇论文提出了一种新的基于多项式时间复杂度的规则提取算法 ECLAIRE，该算法适用于大型 DNN 架构和大型训练数据集，并在乳腺癌预后和粒子探测等任务上表现出超过现有技术的准确性和可理解性。

深度神经网络的高效分解规则提取

Efficient Decompositional Rule Extraction for Deep Neural Networks

A question answering system that in addition to providing an answer provides
an explanation of the reasoning that leads to that answer has potential
advantages in terms of debuggability, extensibility and trust. To this end, we
propose QED, a linguistically informed, extensible framework for explanations
in question answering. A QED explanation specifies the relationship between a
question and answer according to formal semantic notions such as referential
equality, sentencehood, and entailment. We describe and publicly release an
expert-annotated dataset of QED explanations built upon a subset of the Google
Natural Questions dataset, and report baseline models on two tasks -- post-hoc
explanation generation given an answer, and joint question answering and
explanation generation. In the joint setting, a promising result suggests that
training on a relatively small amount of QED data can improve question
answering. In addition to describing the formal, language-theoretic motivations
for the QED approach, we describe a large user study showing that the presence
of QED explanations significantly improves the ability of untrained raters to
spot errors made by a strong neural QA baseline.

QED 是一个基于语言学的可扩展框架，为问答提供解释，提高了调试能力、扩展性和信任度，可以根据规范的语义概念如引用相等性、句子性和蕴含关系说明问题和答案之间的关系，进行问波应答和解释生成的试验表明，训练涉及 QED 数据的模型可以显著提高问答能力，在大量测试中，通过 QED 解释可以显著提高非专业用户改正强神经基线所犯错误的能力。

QED: 问答解释的框架和数据集

QED: A Framework and Dataset for Explanations in Question Answering

The practical impact of deep learning on complex supervised learning problems
has been significant, so much so that almost every Artificial Intelligence
problem, or at least a portion thereof, has been somehow recast as a deep
learning problem. The applications appeal is significant, but this appeal is
increasingly challenged by what some call the challenge of explainability, or
more generally the more traditional challenge of debuggability: if the outcomes
of a deep learning process produce unexpected results (e.g., less than expected
performance of a classifier), then there is little available in the way of
theories or tools to help investigate the potential causes of such unexpected
behavior, especially when this behavior could impact people's lives. We
describe a preliminary framework to help address this issue, which we call
"deep visual explanation" (DVE). "Deep," because it is the development and
performance of deep neural network models that we want to understand. "Visual,"
because we believe that the most rapid insight into a complex multi-dimensional
model is provided by appropriate visualization techniques, and "Explanation,"
because in the spectrum from instrumentation by inserting print statements to
the abductive inference of explanatory hypotheses, we believe that the key to
understanding deep learning relies on the identification and exposure of
hypotheses about the performance behavior of a learned deep model. In the
exposition of our preliminary framework, we use relatively straightforward
image classification examples and a variety of choices on initial configuration
of a deep model building scenario. By careful but not complicated
instrumentation, we expose classification outcomes of deep models using
visualization, and also show initial results for one potential application of
interpretability.

研究了深度学习方法面临的可解释性困境，提出了一种叫做 “深度视觉解释” 的框架，通过可视化技术来识别和暴露深度学习模型性能行为的假设，以提高模型的可解释性和可调试性，并展示了初步的可解释性应用实验结果。