  Machine learning practitioners often end up tunneling on low-level technical
details like model architectures and performance metrics. Could early model
development instead focus on high-level questions of which factors a model
ought to pay attention to? Inspired by the practice of sketching in design,
which distills ideas to their minimal representation, we introduce model
sketching: a technical framework for iteratively and rapidly authoring
functional approximations of a machine learning model's decision-making logic.
Model sketching refocuses practitioner attention on composing high-level,
human-understandable concepts that the model is expected to reason over (e.g.,
profanity, racism, or sarcasm in a content moderation task) using zero-shot
concept instantiation. In an evaluation with 17 ML practitioners, model
sketching reframed thinking from implementation to higher-level exploration,
prompted iteration on a broader range of model designs, and helped identify
gaps in the problem formulation$\unicode{x2014}$all in a fraction of the time
ordinarily required to build a model.
