Toward Personalized Explainable AI

人工智能社区越来越感兴趣的是了解如何构建除了执行有用的任务外，还被用户很好地接受和信任的构件。不可否认，人工智能系统的可解释性可以成为接受和信任的重要因素。然而，对可解释性、接受性和信任之间的实际关系以及哪些因素可能影响这种关系的理解仍然有限。特别是，尽管现有的关于可解释人工智能(XAI)的研究表明，让人工智能系统向最终用户解释其内部工作原理有助于提高透明度、可解释性和信任。也有结果表明，这种解释并不总是所有用户都想要的或对所有用户都有利的。这些结果表明，XAI的研究需要超越一刀切的解释，研究能够根据用户的特定需求对其行为进行个性化解释的人工智能系统。人们普遍同意，这种需求可能取决于背景，例如，人工智能应用的类型和目标任务的关键程度，但也有证据表明，在相同的背景下，用户差异在确定解释何时和如何有用和有效方面发挥了作用。这些结果要求我们有必要研究个性化的XAI，即如何创建理解谁、何时以及如何为他们的行动和决定提供有效解释的人工智能系统。这就是这项提议的目标。几十年来，人工智能驱动的个性化一直是一个活跃的研究领域，涵盖了推荐系统、智能辅导系统、对话代理和情感感知系统等领域。为了提供个性化，人工智能系统需要具有自适应循环，在该自适应循环中，人工智能系统通过从可用观察推断相关的用户属性来获取其用户的模型，并决定如何相应地个性化其行为。在本提案中，为了最多有利于交互的目标，我们将解释框架为自适应循环中的另一个个性化元素，其中系统基于其对具体相关的用户属性的最佳理解来确定是否以及如何向用户解释其行为，以评估对解释的需求。这些相关属性是什么，人工智能系统如何评估它们，以及它如何通过充分的个性化解释做出回应，这些在很大程度上仍是未知的。拟议的研究旨在为填补这些空白做出贡献。

The AI community is increasingly interested in understanding how to build artifacts that, in addition to performing useful tasks, are well accepted and trusted by their users. It is undeniable that the explainability of an AI system can be an important factor for acceptance and trust. However, there is still limited understanding of the actual relationship between explainability, acceptance, and trust and which factors might impact this relationship. In particular, although existing research on Explainable AI (XAI) suggests that having AI systems explain their inner workings to their end users can help foster transparency, interpretability, and trust. there are also results suggesting that such explanations are not always wanted by or beneficial for all users. These results indicate that research in XAI needs to go beyond one-size-fits-all ex-planations and investigate AI systems that can personalize explanations of their behaviors to the user's specific needs. There is general agreement that such needs may depend on context, e.g., the type of AI application and criticality of the targeted tasks, but there is also evidence that, given the same context, user differences play a role in defining when and how explanations may be useful and effective. These results call for the need to investigate personalized XAI, namely how to create AI systems that understand to whom, when, and how to deliver effective explanations of their actions and decisions. This is the objective of this proposal. AI-driven personalization has been an active field of research for several decades, spanning fields such as recommender systems, intelligent-tutoring systems, conversational agents, and affect-aware systems. To provide personalization, an AI system needs to have an adaptive loop in which it acquires a model of its user by inferring relevant user properties from available observations and decides how to personalize its behavior accordingly, to favor at best the goal of the interaction In this proposal, we frame explanations as yet another element of personalization in the adaptive loop, where the system ascertains if and how to explain its behavior to the user based on its best understanding of user properties specifically relevant to evaluate the need for explanation. What these relevant properties are, how an AI system can assess them and how it can respond with adequate personalization of explanations is all still largely unknown. The proposed research aims to contribute to filling these gaps