论文标题
limeade：从AI解释到建议
LIMEADE: From AI Explanations to Advice Taking
论文作者
论文摘要
以人为中心的AI研究表明了可以解释其预测的系统的好处。允许人工智能以解释的方式从人类那里获得建议的方法也同样有用。尽管这两种功能都是针对透明学习模型（例如，线性模型和GA $^2 $ ms）的完善，而最近的技术（例如，石灰和外形）可以为不透明的模型产生解释，但很少关注不透明模型的建议方法。本文介绍了Limeade，这是第一个转化正面和负面建议（使用高级词汇表达的一般框架，例如事后解释所采用的词汇），以更新为任意的，基本的不透明模型。我们通过对两个广泛领域的70个现实世界模型进行案例研究来证明我们的方法的普遍性：图像分类和文本建议。与图像分类域的严格基线相比，我们显示了我们的方法提高了准确性。对于文本方式，我们将框架应用于公共网站上的科学论文的神经推荐系统；我们的用户研究表明，我们的框架会导致更高的感知用户控制，信任和满意度。
Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA$^2$Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, little attention has been given to advice methods for opaque models. This paper introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post-hoc explanations) into an update to an arbitrary, underlying opaque model. We demonstrate the generality of our approach with case studies on seventy real-world models across two broad domains: image classification and text recommendation. We show our method improves accuracy compared to a rigorous baseline on the image classification domains. For the text modality, we apply our framework to a neural recommender system for scientific papers on a public website; our user study shows that our framework leads to significantly higher perceived user control, trust, and satisfaction.
