Mounting evidence in explainability for artificial intelligence (XAI) research suggests that good explanations should be tailored to individual tasks and should relate to concepts relevant to the task. However, building task specific explanations is time consuming and requires domain expertise which can be difficult to integrate into generic XAI methods. A promising approach towards designing useful task specific explanations with domain experts is based on compositionality of semantic concepts. Here, we present a novel approach that enables domain experts to quickly create concept-based explanations for computer vision tasks intuitively via natural language. Leveraging recent progress in deep generative methods we propose to generate visual concept-based prototypes via text-to-image methods. These prototypes are then used to explain predictions of computer vision models via a simple k-Nearest-Neighbors routine. The modular design of CoProNN is simple to implement, it is straightforward to adapt to novel tasks and allows for replacing the classification and text-to-image models as more powerful models are released. The approach can be evaluated offline against the ground-truth of predefined prototypes that can be easily communicated also to domain experts as they are based on visual concepts. We show that our strategy competes very well with other concept-based XAI approaches on coarse grained image classification tasks and may even outperform those methods on more demanding fine grained tasks. We demonstrate the effectiveness of our method for human-machine collaboration settings in qualitative and quantitative user studies. All code and experimental data can be found in our GitHub $\href{https://github.com/TeodorChiaburu/beexplainable}{repository}$.

翻译：人工智能可解释性（XAI）研究中的大量证据表明，良好的解释应针对具体任务定制，并与任务相关的概念相关联。然而，构建任务特定的解释既耗时又需要领域专业知识，这往往难以融入通用的XAI方法中。一种有前景的、与领域专家共同设计实用任务特定解释的方法基于语义概念的组合性。本文提出了一种新颖方法，使领域专家能够通过自然语言直观地快速创建基于概念的视觉任务解释。借助深度生成方法的最新进展，我们提出通过文本到图像方法生成基于视觉概念的原型。这些原型随后通过简单的k近邻算法用于解释计算机视觉模型的预测结果。CoProNN的模块化设计易于实现，可轻松适应新任务，并允许随着更强大模型的发布替换分类模型和文本到图像模型。该方法可基于预定义原型（基于视觉概念易于与领域专家沟通）的基准真相进行离线评估。我们表明，该策略在粗粒度图像分类任务上与其他基于概念的XAI方法表现相当，在更具挑战性的细粒度任务上甚至可能超越这些方法。我们通过定性和定量的用户研究证明了该方法在人机协作场景中的有效性。所有代码和实验数据均可在我们的GitHub存储库中获取。