When used in the context of decision theory, feature importance expresses how
much changing the value of a feature can change the model outcome (or the
utility of the outcome), compared to other features. Feature importance should
not be confused with the feature influence used by most state-of-the-art
post-hoc Explainable AI methods. Contrary to feature importance, feature
influence is measured against a reference level or baseline. The Contextual
Importance and Utility (CIU) method provides a unified definition of global and
local feature importance that is applicable also for post-hoc explanations,
where the value utility concept provides instance-level assessment of how
favorable or not a feature value is for the outcome. The paper shows how CIU
can be applied to both global and local explainability, assesses the fidelity
and stability of different methods, and shows how explanations that use
contextual importance and contextual utility can provide more expressive and
flexible explanations than when using influence only.

这篇论文提供了一种统一的定义，可以应用于后续解释，评估不同方法的忠实度和稳定性，并且展示了使用上下文重要性和效用可以提供比只使用影响力时更富表达力和灵活性的解释。

特征重要性与特征影响：对可解释人工智能的意义

Feature Importance versus Feature Influence and What It Signifies for  Explainable AI

Motivated by the need to audit complex and black box models, there has been
extensive research on quantifying how data features influence model
predictions. Feature influence can be direct (a direct influence on model
outcomes) and indirect (model outcomes are influenced via proxy features).
Feature influence can also be expressed in aggregate over the training or test
data or locally with respect to a single point. Current research has typically
focused on one of each of these dimensions. In this paper, we develop
disentangled influence audits, a procedure to audit the indirect influence of
features. Specifically, we show that disentangled representations provide a
mechanism to identify proxy features in the dataset, while allowing an explicit
computation of feature influence on either individual outcomes or
aggregate-level outcomes. We show through both theory and experiments that
disentangled influence audits can both detect proxy features and show, for each
individual or in aggregate, which of these proxy features affects the
classifier being audited the most. In this respect, our method is more powerful
than existing methods for ascertaining feature influence.

本文提出了一种评估模型特征影响的新方法，利用消解表示来识别数据集中的代理特征，并显式计算特征对个体和聚合结果的影响，结果表明该方法比现有方法更加有效。

解耦影响：使用解耦表示审计模型预测

Disentangling Influence: Using Disentangled Representations to Audit  Model Predictions

Data-trained predictive models see widespread use, but for the most part they
are used as black boxes which output a prediction or score. It is therefore
hard to acquire a deeper understanding of model behavior, and in particular how
different features influence the model prediction. This is important when
interpreting the behavior of complex models, or asserting that certain
problematic attributes (like race or gender) are not unduly influencing
decisions.
In this paper, we present a technique for auditing black-box models, which
lets us study the extent to which existing models take advantage of particular
features in the dataset, without knowing how the models work. Our work focuses
on the problem of indirect influence: how some features might indirectly
influence outcomes via other, related features. As a result, we can find
attribute influences even in cases where, upon further direct examination of
the model, the attribute is not referred to by the model at all.
Our approach does not require the black-box model to be retrained. This is
important if (for example) the model is only accessible via an API, and
contrasts our work with other methods that investigate feature influence like
feature selection. We present experimental evidence for the effectiveness of
our procedure using a variety of publicly available datasets and models. We
also validate our procedure using techniques from interpretable learning and
feature selection, as well as against other black-box auditing procedures.

本文提出了一种审核黑盒模型的技术，旨在研究现有模型利用特定数据集中的哪些特征，即使在直接审查模型时该特征并未被引用，我们仍然可以找到其属性的影响。该方法不需要重新训练黑盒模型。