LIMEADE：从AI咨询到采纳建议
LIMEADE: From AI Explanations to Advice Taking
摘要：
以人为中心的人工智能研究已经表明，可以解释其预测的系统的好处。允许人工智能接受人类建议以回应解释的方法也同样有用。虽然这两种能力都是针对透明学习模型开发的（例如，线性模型和GA $^2$ M），以及最近的技术（例如，LIME和SHAP）可以为不透明模型生成解释，但很少关注不透明模型的建议方法。本文介绍了LIMEADE，第一个通用框架，翻译的积极和消极的建议（使用高层次的词汇，如采用事后解释）到一个任意的，潜在的不透明模型的更新。我们展示了我们的方法的通用性与案例研究的70个现实世界模型在两个广泛的领域：图像分类和文本推荐。我们表明，我们的方法提高了准确性相比，严格的基线上的图像分类域。对于文本模态，我们将我们的框架应用于公共网站上科学论文的神经推荐系统;我们的用户研究表明，我们的框架可以显著提高感知用户控制，信任和满意度。
英文摘要：
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.
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