TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
复制标题
DOI：
--
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Sameer Singh
中科院分区：
文献类型：
--
作者：
Sameer Singh
机器学习（ML）模型越来越多地用于在现实世界中做出关键决策，但它们变得更加复杂，使它们更难理解使用这些解释性技术，因为它们通常不知道要选择哪一个以及如何解释这项工作的结果。通过引入TalkTomodel：通过对话来解释机器学习模型的交互式对话系统，TalkTomodel由三个关键组成部分组成：1）自然语言接口，用于进行对话适应任何表格模型和数据集，解释自然语言，将其映射到适当的解释，并生成文本响应，并3）构建解释的执行组件。在人类的现实评估中，有73％在疾病预测任务中解释的点击系统，85％的ML专业人士同意的TalkTomodel更容易用于计算说明。实践者的解释工具
Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To this end, researchers have proposed several techniques to explain model predictions. However, practitioners struggle to use these explainability techniques because they often do not know which one to choose and how to interpret the results of the explanations. In this work, we address these challenges by introducing TalkToModel: an interactive dialogue system for explaining machine learning models through conversations. Specifically, TalkToModel comprises of three key components: 1) a natural language interface for engaging in conversations, making ML model explainability highly accessible, 2) a dialogue engine that adapts to any tabular model and dataset, interprets natural language, maps it to appropriate explanations, and generates text responses, and 3) an execution component that constructs the explanations. We carried out extensive quantitative and human subject evaluations of TalkToModel. Overall, we found the conversational system understands user inputs on novel datasets and models with high accuracy, demonstrating the system’s capacity to generalize to new situations. In real-world evaluations with humans, 73% of healthcare workers (e.g., doctors and nurses) agreed they would use TalkToModel over baseline point-and-click systems for explain-ability in a disease prediction task, and 85% of ML professionals agreed TalkToModel was easier to use for computing explanations. Our findings demonstrate that TalkToModel is more effective for model explainability than existing systems, introducing a new category of explainability tools for practitioners. 1