  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 explainability
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. Code &
demo released here: https://github.com/dylan-slack/TalkToModel.
