The ability to explain why a machine learning model arrives at a particular
prediction is crucial when used as decision support by human operators of
critical systems. The provided explanations must be provably correct, and
preferably without redundant information, called minimal explanations. In this
paper, we aim at finding explanations for predictions made by tree ensembles
that are not only minimal, but also minimum with respect to a cost function.
To this end, we first present a highly efficient oracle that can determine
the correctness of explanations, surpassing the runtime performance of current
state-of-the-art alternatives by several orders of magnitude when computing
minimal explanations.
Secondly, we adapt an algorithm called MARCO from related works (calling it
m-MARCO) for the purpose of computing a single minimum explanation per
prediction, and demonstrate an overall speedup factor of two compared to the
MARCO algorithm which enumerates all minimal explanations.
Finally, we study the obtained explanations from a range of use cases,
leading to further insights of their characteristics. In particular, we observe
that in several cases, there are more than 100,000 minimal explanations to
choose from for a single prediction. In these cases, we see that only a small
portion of the minimal explanations are also minimum, and that the minimum
explanations are significantly less verbose, hence motivating the aim of this
work.

本文旨在通过一种高效的 Oracle 和 MARCO 算法改进，提供一个最小的、满足成本函数的解释，以解释机器学习模型的预测，并对这些解释进行了研究和分析，以进一步了解它们的特点。

基于树集的预测最小成本解释查找

Finding Minimum-Cost Explanations for Predictions made by Tree Ensembles

Text detoxification has the potential to mitigate the harms of toxicity by
rephrasing text to remove offensive meaning, but subtle toxicity remains
challenging to tackle. We introduce MaRCo, a detoxification algorithm that
combines controllable generation and text rewriting methods using a Product of
Experts with autoencoder language models (LMs). MaRCo uses likelihoods under a
non-toxic LM (expert) and a toxic LM (anti-expert) to find candidate words to
mask and potentially replace. We evaluate our method on several subtle toxicity
and microaggressions datasets, and show that it not only outperforms baselines
on automatic metrics, but MaRCo's rewrites are preferred 2.1 $\times$ more in
human evaluation. Its applicability to instances of subtle toxicity is
especially promising, demonstrating a path forward for addressing increasingly
elusive online hate.

介绍了 MaRCo 算法，该算法采用 Product of Experts 和自编码语言模型相结合的方式，通过在非有毒和有毒语言模型下评估可能要屏蔽和替换的候选单词，能够有效地解决细微的毒性挑战，即使在人类评估中也要比基线方法更受欢迎，证明了其在解决日益逃避的网络仇恨问题上具有广泛的应用前景。