Model Cards for Model Reporting (Mitchell et al. 2019) has 1323 citations listed in Google Scholar.

The paper introduces a way to summarize the characteristics of a machine learning model in a structured format, providing a framework that aims to improve transparency, trustworthiness, and accountability. These model cards serve as an easily digestible, yet comprehensive, document that includes information about a model's training data, limitations, intended use-cases, and performance metrics, among other details. The initiative is especially relevant for stakeholders who may not have a technical background but need to understand the capabilities and constraints of a model, such as policy makers, end-users, and oversight committees. By emphasizing the need for such standardized reporting, the paper has opened up new discussions on ethical considerations in machine learning, and has led to the adoption of similar documentation practices in both academic and industry settings.

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