The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
复制标题
可解释人工智能 (XAI) 的务实转向
作者：
Andrés Páez
在本文中，我认为，在人工智能中寻找可解释的模型和可解释的决策必须重新制定更广泛的项目，提供一个务实和自然主义的解释人工智能。直观地说，提供模型或决策解释的目的是使其利益相关者能够理解。但是，如果没有事先掌握一个智能体理解一个模型或一个决策意味着什么，解释性策略将缺乏一个明确的目标。除了为XAI提供更清晰的目标外，专注于理解还可以让我们放松解释的事实性条件，这在许多机器学习模型中是不可能实现的。而不是把重点放在确定模型与理解模型的方法和设备之间最佳匹配的实用条件上。在对哲学和哲学中讨论的不同类型的理解进行检查之后，根据心理学文献，我得出结论，解释或近似模型不仅提供了实现机器学习模型的客观理解的最佳方式，而且也是实现事后可解释性的必要条件。这一结论部分是基于纯粹的功能主义方法的缺点，事后的可解释性，似乎是占主导地位的最新文献。
In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature.