Interpretability methods that utilise local surrogate models (e.g. LIME) are
very good at describing the behaviour of the predictive model at a point of
interest, but they are not guaranteed to extrapolate to the local region
surrounding the point. However, overfitting to the local curvature of the
predictive model and malicious tampering can significantly limit extrapolation.
We propose an anchor-based algorithm for identifying regions in which local
explanations are guaranteed to be correct by explicitly describing those
intervals along which the input features can be trusted. Our method produces an
interpretable feature-aligned box where the prediction of the local surrogate
model is guaranteed to match the predictive model. We demonstrate that our
algorithm can be used to find explanations with larger guarantee regions that
better cover the data manifold compared to existing baselines. We also show how
our method can identify misleading local explanations with significantly poorer
guarantee regions.

我们提出了一种基于锚点的算法，用于确定那些可以保证局部解释正确性的区域，通过明确描述输入特征可信赖的区间来生成可解释的特征对齐框，保证局部代理模型的预测与预测模型相匹配；我们展示了相比现有基线方法，我们的算法能够找到更大的保证区域的解释，更好地涵盖数据流形，并展示了我们的方法如何识别具有较差保证区域的误导性局部解释。

保证解释的区域

Guarantee Regions for Local Explanations

Local Surrogate models have increased in popularity for use in explaining
complex black-box models for diverse types of data, including text, tabular,
and image. One particular algorithm, LIME, continues to see use within the
field of machine learning due to its inherently interpretable explanations and
model-agnostic behavior. But despite continued use, questions about the
stability of LIME persist. Stability, a property where similar instances result
in similar explanations, has been shown to be lacking in explanations generated
for tabular and image data, both of which are continuous domains. Here we
explore the stability of LIME's explanations generated on textual data and
confirm the trend of instability shown in previous research for other data
types.

本文验证了先前文献指出的 LIME 对于无序数据稳定性较低的趋势，并在文本数据上探索了 LIME 的稳定性。

LIME 模型在文本分类中解释结果的稳定性分析：对抗扰动的探究

Are Your Explanations Reliable? Investigating the Stability of LIME in  Explaining Textual Classification Models via Adversarial Perturbation

Local surrogate models, to approximate the local decision boundary of a
black-box classifier, constitute one approach to generate explanations for the
rationale behind an individual prediction made by the back-box. This paper
highlights the importance of defining the right locality, the neighborhood on
which a local surrogate is trained, in order to approximate accurately the
local black-box decision boundary. Unfortunately, as shown in this paper, this
issue is not only a parameter or sampling distribution challenge and has a
major impact on the relevance and quality of the approximation of the local
black-box decision boundary and thus on the meaning and accuracy of the
generated explanation. To overcome the identified problems, quantified with an
adapted measure and procedure, we propose to generate surrogate-based
explanations for individual predictions based on a sampling centered on
particular place of the decision boundary, relevant for the prediction to be
explained, rather than on the prediction itself as it is classically done. We
evaluate the novel approach compared to state-of-the-art methods and a
straightforward improvement thereof on four UCI datasets.

本研究针对黑盒分类器的应用，提出了一种基于本地替代模型的解释生成方法，强调了定义正确的邻域对于准确逼近本地黑盒决策边界的重要性。该方法通过在决策边界上的特定区域进行采样，生成了一个新型的替换方法，并在四个 UCI 数据集上进行了评估，证实了其有效性。