论文标题
让我们去外星动物园：引入一个实验框架来研究机器学习的反事实解释的可用性
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning
论文作者
论文摘要
为了促进机器学习的实用性和问责制（ML），除了评估其性能外，必须解释模型的决定。因此，可解释的人工智能（XAI）的领域已重新浮出水面，这是一个积极研究的主题，提供了解决自动决策的“如何”和“为什么”的方法。在该领域内，反事实解释（CFE）已获得了相当大的吸引力，作为一种心理扎根的方法来产生事后解释。为此，CFE强调了模型输入的变化将以特定方式改变其预测。但是，尽管引入了许多CFE方法，但它们的可用性尚未在人类层面得到彻底验证。因此，为了推进XAI领域，我们介绍了Alien Zoo，这是一个引人入胜的，基于网络和游戏启发的实验框架。外星动物园提供了评估CFE可用性以从自动化系统中获得新知识的手段，以在域名环境中针对新手用户。作为概念证明，我们在用户研究中证明了这种方法的实际功效和可行性。我们的结果表明，用户从收到CFE中受益，而没有任何解释，无论是在拟议的迭代学习任务和主观可用性中的客观绩效方面。通过这项工作，我们旨在为研究小组和从业人员配备能够轻松进行受控且能力良好的用户研究的手段，以补充其原本以技术为导向的工作。因此，为了重现研究，我们提供了整个代码，以及基础模型和用户数据。
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the "how" and "why" of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generate post-hoc explanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level. Thus, to advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study. Our results suggest that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability. With this work, we aim to equip research groups and practitioners with the means to easily run controlled and well-powered user studies to complement their otherwise often more technology-oriented work. Thus, in the interest of reproducible research, we provide the entire code, together with the underlying models and user data.
