Despite the progress observed with model-agnostic explainable AI (XAI), it is
the case that model-agnostic XAI can produce incorrect explanations. One
alternative are the so-called formal approaches to XAI, that include
PI-explanations. Unfortunately, PI-explanations also exhibit important
drawbacks, the most visible of which is arguably their size. The computation of
relevant features serves to trade off probabilistic precision for the number of
features in an explanation. However, even for very simple classifiers, the
complexity of computing sets of relevant features is prohibitive. This paper
investigates the computation of relevant sets for Naive Bayes Classifiers
(NBCs), and shows that, in practice, these are easy to compute. Furthermore,
the experiments confirm that succinct sets of relevant features can be obtained
with NBCs.

本文研究了朴素贝叶斯分类器中相关特征的计算，并表明这些特征的简明集合可以很容易地获得，从而解决了形式化方法在可解释性人工智能领域的重要难题。

计算解释 NBCs 的相关特征

On Computing Relevant Features for Explaining NBCs

As machine learning is increasingly used to make real-world decisions, recent
research efforts aim to define and ensure fairness in algorithmic decision
making. Existing methods often assume a fixed set of observable features to
define individuals, but lack a discussion of certain features not being
observed at test time. In this paper, we study fairness of naive Bayes
classifiers, which allow partial observations. In particular, we introduce the
notion of a discrimination pattern, which refers to an individual receiving
different classifications depending on whether some sensitive attributes were
observed. Then a model is considered fair if it has no such pattern. We propose
an algorithm to discover and mine for discrimination patterns in a naive Bayes
classifier, and show how to learn maximum likelihood parameters subject to
these fairness constraints. Our approach iteratively discovers and eliminates
discrimination patterns until a fair model is learned. An empirical evaluation
on three real-world datasets demonstrates that we can remove exponentially many
discrimination patterns by only adding a small fraction of them as constraints.

该研究论文探讨了利用朴素贝叶斯分类器实现公平，提出了歧视模式的概念，介绍了一种发现和挖掘歧视模式的算法，并展示了一种学习最大似然参数以符合公平约束的方法，通过在三个真实世界数据集上进行实证评估表明，只增加少量约束即可消除指数级别的歧视模式。

发现并消除歧视模式以学习公平朴素贝叶斯分类器

Learning Fair Naive Bayes Classifiers by Discovering and Eliminating  Discrimination Patterns

Naive Bayes classifiers, a family of classifiers that are based on the
popular Bayes' probability theorem, are known for creating simple yet well
performing models, especially in the fields of document classification and
disease prediction. In this article, we will look at the main concepts of naive
Bayes classification in the context of document categorization.

本文主要介绍纳伊夫 - 贝叶斯分类器在文档分类中的主要概念和应用。

朴素贝叶斯和文本分类 I - 介绍和理论

Naive Bayes and Text Classification I - Introduction and Theory

Bayesian network classifiers are used in many fields, and one common class of
classifiers are naive Bayes classifiers. In this paper, we introduce an
approach for reasoning about Bayesian network classifiers in which we
explicitly convert them into Ordered Decision Diagrams (ODDs), which are then
used to reason about the properties of these classifiers. Specifically, we
present an algorithm for converting any naive Bayes classifier into an ODD, and
we show theoretically and experimentally that this algorithm can give us an ODD
that is tractable in size even given an intractable number of instances. Since
ODDs are tractable representations of classifiers, our algorithm allows us to
efficiently test the equivalence of two naive Bayes classifiers and
characterize discrepancies between them. We also show a number of additional
results including a count of distinct classifiers that can be induced by
changing some CPT in a naive Bayes classifier, and the range of allowable
changes to a CPT which keeps the current classifier unchanged.

本文介绍了一种基于 Ordered Decision Diagrams（ODDs）的贝叶斯网络分类器的推理方法，包括将任何朴素贝叶斯分类器转换成 ODDs 的算法，以及使用 ODDs 进行等价性测试和 CPT 更改范围的特性分析。