The use of eXplainable Artificial Intelligence (XAI) systems has introduced a
set of challenges that need resolution. Herein, we focus on how to correctly
select an XAI method, an open questions within the field. The inherent
difficulty of this task is due to the lack of a ground truth. Several authors
have proposed metrics to approximate the fidelity of different XAI methods.
These metrics lack verification and have concerning disagreements. In this
study, we proposed a novel methodology to verify fidelity metrics, using a
well-known transparent model, namely a decision tree. This model allowed us to
obtain explanations with perfect fidelity. Our proposal constitutes the first
objective benchmark for these metrics, facilitating a comparison of existing
proposals, and surpassing existing methods. We applied our benchmark to assess
the existing fidelity metrics in two different experiments, each using public
datasets comprising 52,000 images. The images from these datasets had a size a
128 by 128 pixels and were synthetic data that simplified the training process.
All metric values, indicated a lack of fidelity, with the best one showing a 30
\% deviation from the expected values for perfect explanation. Our
experimentation led us to conclude that the current fidelity metrics are not
reliable enough to be used in real scenarios. From this finding, we deemed it
necessary to development new metrics, to avoid the detected problems, and we
recommend the usage of our proposal as a benchmark within the scientific
community to address these limitations.

使用透明模型决策树作为基准，研究提出了一种新的测度指标验证 XAI 方法的准确性，结果显示了现有的测度指标在真实场景中不可靠，并建议开发新的指标以解决这些问题。

关于 XAI 的保真度度量的综合研究

A comprehensive study on fidelity metrics for XAI

Motivation: We explored how explainable AI (XAI) can help to shed light into
the inner workings of neural networks for protein function prediction, by
extending the widely used XAI method of integrated gradients such that latent
representations inside of transformer models, which were finetuned to Gene
Ontology term and Enzyme Commission number prediction, can be inspected too.
Results: The approach enabled us to identify amino acids in the sequences that
the transformers pay particular attention to, and to show that these relevant
sequence parts reflect expectations from biology and chemistry, both in the
embedding layer and inside of the model, where we identified transformer heads
with a statistically significant correspondence of attribution maps with ground
truth sequence annotations (e.g., transmembrane regions, active sites) across
many proteins. Availability and Implementation: Source code can be accessed at
this https URL .

我们探索了可解释的人工智能（XAI）如何帮助揭示神经网络在蛋白质功能预测中的内部运作，通过扩展广泛使用的 XAI 方法 —— 集成梯度，使得可以检查 Gene Ontology 术语和酶委员会编号预测中微调的 Transformer 模型内部的潜在表示。

探寻 Transformer 模型在蛋白质功能预测中的内部机制

Insights Into the Inner Workings of Transformer Models for Protein  Function Prediction

Motivations for methods in explainable artificial intelligence (XAI) often
include detecting, quantifying and mitigating bias, and contributing to making
machine learning models fairer. However, exactly how an XAI method can help in
combating biases is often left unspecified. In this paper, we briefly review
trends in explainability and fairness in NLP research, identify the current
practices in which explainability methods are applied to detect and mitigate
bias, and investigate the barriers preventing XAI methods from being used more
widely in tackling fairness issues.

本文简要回顾自然语言处理领域中解释性和公平性的研究趋势，识别出目前通过解释性方法来检测和减轻偏见的实践，调查 XAI 方法在解决公平问题方面广泛应用的障碍。