In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity
of the term "explanation" in explainable AI (XAI) can be solved by adopting any
of four different extant accounts of explanation in the philosophy of science:
the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New
Mechanist models. In this chapter, I show that the authors' claim that these
accounts can be applied to deep neural networks as they would to any natural
phenomenon is mistaken. I also provide a more general argument as to why the
notion of explainability as it is currently used in the XAI literature bears
little resemblance to the traditional concept of scientific explanation. It
would be more fruitful to use the label "understandable AI" to avoid the
confusion that surrounds the goal and purposes of XAI. In the second half of
the chapter, I argue for a pragmatic conception of understanding that is better
suited to play the central role attributed to explanation in XAI. Following
Kuorikoski & Ylikoski (2015), the conditions of satisfaction for understanding
an ML system are fleshed out in terms of an agent's success in using the
system, in drawing correct inferences from it.

深度神经网络的可解释性问题可以通过采用不同的科学解释模型，避免当前解释模糊性而产生的困惑，更有助于用 “可理解的人工智能” 标签避免 XAI 的混淆。

剔除 XAI 中的 X：关于可理解人工智能的呼吁

Axe the X in XAI: A Plea for Understandable AI

Scientific understanding is a fundamental goal of science, allowing us to
explain the world. There is currently no good way to measure the scientific
understanding of agents, whether these be humans or Artificial Intelligence
systems. Without a clear benchmark, it is challenging to evaluate and compare
different levels of and approaches to scientific understanding. In this
Roadmap, we propose a framework to create a benchmark for scientific
understanding, utilizing tools from philosophy of science. We adopt a
behavioral notion according to which genuine understanding should be recognized
as an ability to perform certain tasks. We extend this notion by considering a
set of questions that can gauge different levels of scientific understanding,
covering information retrieval, the capability to arrange information to
produce an explanation, and the ability to infer how things would be different
under different circumstances. The Scientific Understanding Benchmark (SUB),
which is formed by a set of these tests, allows for the evaluation and
comparison of different approaches. Benchmarking plays a crucial role in
establishing trust, ensuring quality control, and providing a basis for
performance evaluation. By aligning machine and human scientific understanding
we can improve their utility, ultimately advancing scientific understanding and
helping to discover new insights within machines.

本文提出了一种基于科学哲学的框架，利用行为学概念来评估机器智能和人类的科学理解水平，并通过一组测试建立了科学理解基准，以便评估和比较不同方法和水平。

人类和机器科学理解的基准创建

Towards a Benchmark for Scientific Understanding in Humans and Machines

The currently dominating artificial intelligence and machine learning
technology, neural networks, builds on inductive statistical learning. Neural
networks of today are information processing systems void of understanding and
reasoning capabilities, consequently, they cannot explain promoted decisions in
a humanly valid form. In this work, we revisit and use fundamental philosophy
of science theories as an analytical lens with the goal of revealing, what can
be expected, and more importantly, not expected, from methods that aim to
explain decisions promoted by a neural network. By conducting a case study we
investigate a selection of explainability method's performance over two mundane
domains, animals and headgear. Through our study, we lay bare that the
usefulness of these methods relies on human domain knowledge and our ability to
understand, generalise and reason. The explainability methods can be useful
when the goal is to gain further insights into a trained neural network's
strengths and weaknesses. If our aim instead is to use these explainability
methods to promote actionable decisions or build trust in ML-models they need
to be less ambiguous than they are today. In this work, we conclude from our
study, that benchmarking explainability methods, is a central quest towards
trustworthy artificial intelligence and machine learning.

本文以科学哲学理论作为分析视角，研究解释性方法在神经网络性能评估中的应用，并发现解释性方法的实用性依赖于人类领域知识和理解推理能力。本研究得出结论，基准测试解释性方法是迈向可信人工智能和机器学习的中心任务。